Publications

This letter investigates the coupled control problem in UAV networks utilizing high-frequency hybrid beamsteering. While phased arrays enable rapid electronic scanning, their finite Field of View (FoV) imposes a fundamental constraint that necessitates active mechanical steering of the airframe to maintain connectivity. We propose a decentralized Model Predictive Control (MPC) framework that jointly optimizes trajectory and heading to maximize network sum-capacity subject to safety constraints. Addressing the numerical instability caused by fast-fading channel nulls, we introduce a regularized surrogate cost function based on discrete spatial smoothing. Simulation results demonstrate that the proposed algorithm effectively navigates the trade-off between electronic beam tracking and kinematic safety.

This letter investigates the coupled control problem in UA V networks utilizing high-frequency hybrid beamsteering. While phased arrays enable rapid electronic scanning, their finite Field of View (FoV) imposes a fundamental constraint that necessitates active mechanical steering of the airframe to maintain connectivity. We propose a decentralized Model Predic- tive Control (MPC) framework that jointly optimizes trajectory and heading to maximize network sum-capacity subject to safety constraints. Addressing the numerical instability caused by fast- fading channel nulls, we introduce a regularized surrogate cost function based on discrete spatial smoothing. We analytically prove that this approximation bounds the cost curvature, restor- ing the Lipschitz continuity of the gradient. Crucially, we derive a sufficient condition linking this Lipschitz constant to the con- troller gain, guaranteeing the contraction and linear convergence of the distributed best-response dynamics. Simulation results demonstrate that the proposed algorithm effectively navigates the trade-off between electronic beam tracking and kinematic safety, significantly systematically outperforming velocity-aligned baselines.

In this paper, we focus on the estimation of massive Multiple-Input Multiple-Output (mMIMO) channels in high-frequency point-to-point wireless communications between a terrestrial multi-antenna base station, realizing fully digital beamforming, and a multi-antenna Unmanned Aerial Vehicle (UA V), capable of only analog combining via a single reception Radio-Frequency (RF) chain. A wideband channel model, incorporating selectivity which is usually referred to as beam squint and is particularly relevant in high frequency bands, such as millimeter waves and terahertz, is considered. To account for the UA V’s limited capability to collect long training sequences, due to its single-RF reception architecture, and the beam-squint effect at both communication ends, we present a novel channel estimation approach that exploits information provided by the control module of the UA V , namely its state-space vector. The proposed approach comprises a hybrid parametric and non-parametric estimation op- timization framework that is efficiently solved through an iterative algorithm using the alternating direction method of multipliers. Our extensive performance investigations, including comparisons with benchmark schemes, showcase that considerable gains can be achieved if state-space information from the UA V’s control module is appropriately incorporated in the channel estimation process.

Single-carrier (SC) waveforms offer significant ad- vantages in high-frequency communications due to their lower peak-to-average power ratios, mitigating channel and system impairments. While extensive research has addressed dual- wideband fading challenges for OFDM systems in mmWave/THz, the investigation of these effects in SC MIMO transmissions remains limited. This paper focuses on the time-domain estima- tion of MIMO channel matrices for point-to-point systems under dual-wideband fading, encompassing both beam squint and inter- symbol interference (ISI). We propose a unified framework that accounts for beam squint at both the transmitter and receiver, as well as ISI caused by propagation delays, addressing limitations prevalent in single-antenna studies. A novel mixed- integer biconvex model provides a flexible channel estimation framework. It handles diverse delay patterns using a binary vector and decomposes the channel into path-specific matrices for smart environment integration, such as sensing and reflecting surfaces. This semi-parametric approach, estimating delays and separating path gains/angles, offers advantages over conventional methods and enables advanced optimization.

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited.

Joint radar-communications (JRC) benefits from multi-functionality of radar and communication operations using same hardware and radio frequency (RF) spectrum resources. Thus, JRC systems possess very high potential to be employed into the sixth generation (6G) standards. Besides, intelligent re- flecting surfaces have attracted wide attention in communication systems, due to low complexity of implementation. This paper designs a dynamic beamformer for reconfigurable intelligent surfaces (RIS) which maximizes spectral efficiency (SE). We jointly express the mutual information rate for communication and radar entities including a weighting factor which depicts the dominance of one operation over the other. The joint-SE based proposed method optimally selects the RIS subset. Furthermore, when the communication operation takes place the proposed method takes into account the interference occurring from the radar operation and vice-versa. Fractional programming based selection procedure is used for solving the problem of subset selection. Simulation results are presented and compared with different baselines to show effectiveness of the proposed method.

Joint radar-communications (JRC) is considered to be a vital technology in deploying the next generation systems, since its useful in decongestion of the radio frequency (RF) spectrum and utilising the same hardware resources for dual functions. Using JRC systems for dual function generates interference be- tween both the operations which needs to be addressed in future standardization. Furthermore, JRC systems can be advanced by deploying hybrid beamforming which implements fewer number of RF chains than the number of transmit antennas. This paper designs a robust hybrid beamformer for minimizing the interfer- ence of a JRC transmitter via RF chain selection resulting into mutual information maximization. We consider a weighted mutual information for the dual function JRC system and implement a common analog beamformer for both the operations. The mutual information maximization problem is formulated which is non- convex and difficult to solve. The problem is simplified to convex form and solved using Dinkelbach approximation abased fractional programming. The performance of the optimal RF selection based proposed approach is evaluated, compared with baselines and its effectiveness is inferred via numerical results.

The joint radar‐comm unication (JRC) system is env isioned as an emerging sixth gener- ation (6G) technolog y to tack le spectral congest ion and hardwa re limitations by jointly implementing the communication and radar sensing on the same hardwa re platfor m and using the common radio frequency (RF) resources . J oint radar‐comm unication systems with a mult i‐antenna setup leads to higher deg rees of freedom, and hybr id beamfor ming can be exploited to achiev e low er hardwa re complexity than conv entional fully digital systems . This paper aims to design a spectral efficiency maximisation approach for a 6G inclined JR C system with hybrid beamfor ming and m ulti‐antenna setup while considering the interference between commun ication and radar operations and hardwa re impair ments in the system. The rate expressions for communication and radar operations are defined, and the joint spectral efficiency is maximised via optimising the number of RF chains using an efficient selection alg orithm taking into account the interference of one oper - ation to the other and system hardwa re distor tion.

Quantum computing (QC)-assisted algorithms promise exponential increase in the computational efficiency, enabling instant solution of large systems of equations. Mas- sive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems pose extraordinary demands in computa- tional complexity, due to the usage of huge antenna arrays, massive number of users, and ultra low-latency requirements. This work provides an initial discussion on how QC-based algorithms for solving linear systems of equations could be utilized for assisting the basic operations of the transceivers physical-layer, such as the channel estimation. We identify the connections between the amplitude encoding in the quantum domain and the recovery of the channel information.

Nowdays, millimeter wave (mmW ave) direction sensors are being used increasingly as general-purpose radars, since they can provide high-level of accuracy for a variety of situations at low-cost. Via mutliple mmW ave sensors, bearing estimation can be derived to track the position of a target, while in smart environments several sensors can be deployed. In this work, we provide an optimal sensor selection technique, for choosing which sensors to activate for bearing estimation and which not. The proposed approach is decomposed into training phase, where sensor selection is performed, and operational phase, where bearing estimation is obtained. Via simulation results we evaluate the proposed approach compared with the conventional methodology of utilizing all available data streams. I. I N T RO D U C T I O N Collaboration between humans and robots plays a sig- nificant role in smart industries since it helps to achieve improved production and efficiency . However, as the separa- tion between robot and human workstations is removed, this evolution means breaking with established safety procedures [1]. Sensor nodes are used in industrial operations to collect system measurements.

This paper considers a multiple-input multiple- output (MIMO) joint radar-communication (JRC) transmission with hybrid precoding and low resolution digital to analog converters (DACs). An energy efficient radio frequency (RF) chain and DAC bit selection approach is presented for a sub- arrayed hybrid MIMO JRC system. W e introduce a weighting formulation to represent the combined radar-communications information rate. The presented selection mechanism is incorpo- rated with fractional programming to solve an energy efficiency maximization problem for JRC which selects the optimal number of RF chains and DAC bit resolution. Subsequently, a weighted minimization problem to compute the precoding matrices is formulated, which is solved using an alternating minimization approach. The numerical results show the effectiveness of the proposed method in terms of high energy efficiency whilst main- taining good rate and desirable radar beampattern performance. Index T erms—Joint radar-communications, energy efficient, RF chain optimization, low resolution DACs, hybrid precoder . I.

Distributed model-predictive controllers provide a robust way to adjust the acceleration of each platoon vehicle and avoid collisions. This is achieved by transforming the control problem into an iterative, finite-horizon optimization with local constraints. However, the derivation of the global optimal solution is not straightforward. In this paper, first, the consensus cost function is formulated, constrained by minimum distance requirements between the vehicles. Then, the solution is derived via the alternating direction method of multipliers (ADMM), an iterative and robust solver with minimal communication demands. A low-complexity solution is proposed by casting the problem as stochastic control optimization. The developed techniques are evaluated via simulations, where the trajectory of the leading vehicle is generated by an open-source software for autonomous driving (CARLA). I. I NTRODUCTION Cooperative platooning of connected and autonomous vehi- cles (CA Vs) provides an efficient traffic management system, that increases the fuel economy, and enhances the road utility and safety in smart cities and highways [1, 2].

In this article, we aim to design highly energy efficient end-to-end communication for millimeter wave multiple- input multiple-output systems. This is done by jointly optimizing the digital-to-analog converter (DAC)/analog-to-digital converter (ADC) bit resolutions and hybrid beamforming matrices. The novel decomposition of the hybrid precoder and the hybrid combiner to three parts is introduced at the transmitter (TX) and the receiver (RX), respectively, representing the analog precoder/combiner matrix, the DAC/ADC bit resolution matrix and the baseband precoder/combiner matrix. The unknown matrices are computed as a solution to the matrix factorization problem where the optimal fully digital precoder or combiner is approximated by the product of these matrices. A novel and efficient solution based on the alternating direction method of multipliers is proposed to solve these problems at both the TX and the RX. The simulation results show that the proposed solution, where the DAC/ADC bit allocation is dynamic during operation, achieves higher energy efficiency when compared with existing benchmark techniques that use fixed DAC/ADC bit resolutions.

Energy-efficiency (EE) is identified as a key 5G metric and will have a major impact on the hybrid beam- forming system design. The most promising system designs include a reduced number of radio-frequency (RF) chains with digital-to-analog converters (DACs) of lower sampling resolu- tion. However, naive reduction of beamformer components to reduce power consumption typically leads to significant loss of spectral-efficiency (SE). In this paper, we focus on the transmit beamforming (precoding) and we introduce an architecture with low-end components that maximizes the EE while minimizing the effects on SE. This is achieved by the novel design of the analog part of the precoder, where the number of the RF chains is not reduced a priori, but deactivated based on an optimization algorithm. Thus, the problem becomes a subset selection one, where only the RF chains with the optimal SE-EE performance are being activated. The selection algorithm not only determines the optimal number of RF chains to activate but also selects optimally between DACs of randomly-allocated resolution. Through simulations, we verify that the proposed architecture exhibits improved performance when compared with baseline precoding techniques which use a predefined number of RF chains with low-resolution DACs.

Millimeter Wave (mmWave) massive Multiple Input Multi- ple Output (MIMO) channel tracking is a challenging task with Hybrid analog and digital BeamForming (HBF) re- ception architectures. The wireless channel can only be spatially sampled with directive analog beams, which re- sults in lengthy training periods when beam codebooks are large. In this paper, we capitalize on a recently proposed HBF architecture enabling mmWave massive MIMO channel estimation with short beam training overhead, and present a matrix-completion-based channel tracking technique for time correlated HBF receivers. The considered channel track- ing problem is formulated as a constrained multi-objective optimization problem incorporating the low rank and group- sparse properties of the mmWave channel as well as a pop- ular model for its time correlation. We present an efficient algorithm for this estimation problem that is based on the alternating direction method of multipliers. Comparisons of the proposed approach over representative state-of-the- art techniques showcase the relation between the channel time correlation coefficient and the amount of beam training needed for acceptable channel estimation performance.

In this paper, we consider millimeter Wave (mmWave) communications between an Unmanned Aerial Ve- hicle (UA V) and a base station. By assuming that both com- munication nodes are equipped with arrays of multiple antenna elements, we focus on the joint estimation of the UA V position and the Multiple Input Multiple Output (MIMO) channel coefficients. Capitalizing on the line-of-sight signal propagation conditions and the beamspace representation of the wireless channel, we first estimate the UA V position. Using this estimation, we then present a matrix completion formulation for the recovery of the remaining non-line-of-sight components of the MIMO channel matrix, which is efficiently solved via the alternating direction method of multipliers. Selected simulation results for a 28GHz channel model verify that the proposed scheme is beneficial both in terms of estimation accuracy and resources utilization.

Intelligent surfaces comprising of cost effective, nearly pas- sive, and reconfigurable unit elements are lately gaining in- creasing interest due to their potential in enabling fully pro- grammable wireless environments. They are envisioned to offer environmental intelligence for diverse communication objectives, when coated on various objects of the deployment area of interest. To achieve this overarching goal, the chan- nels where the Reconfigurable Intelligent Surfaces (RISs) are involved need to be in principle estimated. However, this is a challenging task with the currently available hardware RIS ar- chitectures requiring lengthy training periods among the net- work nodes utilizing RIS-assisted wireless communication. In this paper, we present a novel RIS architecture comprising of any number of passive reflecting elements, a simple con- troller for their adjustable configuration, and a single Radio Frequency (RF) chain for baseband measurements. Capitaliz- ing on this architecture and assuming sparse wireless channels in the beamspace domain, we present an alternating optimiza- tion approach for explicit estimation of the channel gains at the RIS elements attached to the single RF chain. Represen- tative simulation results demonstrate the channel estimation accuracy and achievable end-to-end performance for various training lengths and numbers of reflecting unit elements.

Millimeter Wave (mmWave) massive Multiple In- put Multiple Output (MIMO) systems realizing directive com- munication over large bandwidths via Hybrid analog and digital BeamForming (HBF) require reliable estimation of the wideband wireless channel. However, the hardware limitations with HBF architectures in conjunction with the short coherence time inherit in mmWave communication render this estimation a challenging task. In this paper, we develop a novel wideband channel estima- tion framework for mmWave massive MIMO systems with HBF reception. The proposed framework jointly exploits the low rank property and the available angular information to provide more accurate channel recovery, especially for short beam training in- tervals. We introduce a novel analog combining architecture that includes a random spatial sampling structure placed before the input of the analog received signals to the digital component of the HBF receiver . This architecture supports the proposed matrix- completion-based estimation approach in providing the sampling set of measurements for recovering the unknown channel matrix.

Nowadays, real-time 3D scanning and reconstruction becomes a requirement for a variety of interactive applications in various fields, including heritage science, gaming, engineering, landscape topography, and medicine. From the

It is well known that large antenna arrays with beamforming capabilities are required to compensate for the high path-loss at millimeter-wave (mmWave) frequencies. Recently, a practical two-stage Rotman lens beamformer has demonstrated increased antenna gain with reduced implementation complexity, since the conventional beam selection network was omitted. In this work, we adopt this system and we investigate its per- formance in terms of symbol estimation when analog-to-digital converters (ADCs) with low-resolution sampling being employed. Although this design is characterized by low-complexity and low- cost, the analog beamformer and the ADCs introduce several impairments to the received signal. To mitigate these effects, we have developed a robust maximum a posteriori (MAP) estimator based on the Expectation-Maximization (EM) iterative algorithm. The proposed algorithm outperforms the conventional EM approach, exhibiting small mean-square-error in the medium to high signal-to-noise ratio regimes. I.

The Industrial IoT (IIoT) is a key element of Industry 4.0, bringing together modern sensor technology, fog - cloud computing platforms, and artificial intelligence (AI) to create smart, self-optimizing industrial equipment and facilities. Though, the scale and sensitivity degree of information con- tinuously increases, giving rise to serious privacy concerns. In this work we address the problem of efficiently and effectively tracking the structure of multivariate streams recorded in a network of IIoT devices. The time varying correlation data values are used to add noise which maximally preserves privacy, in the sense that it is very hard to be removed. T o improve communication efficiency between connected IoT devices, we exploit low rank properties of the correlation matrices, and track the essential correlations from a small subset of correlation values estimated by a subset of network nodes. Extensive simulation studies, validate the correctness, efficiency, and effectiveness of our approach in terms of computational complexity, transmission energy efficiency and privacy preservation. I.

Hybrid analog and digital BeamForming (HBF) transceiver architectures realizing directive communication over large bandwidths in millimeter Wave (mmWave) massive Multi- ple Input Multiple Output (MIMO) systems require the availabil- ity of accurate channel estimation. This is, however, a challenging task mainly due to the short channel coherence time and the hardware limitations imposed by HBF architectures. In this paper, we capitalize on recent matrix completion tools and develop a novel wideband channel estimation technique for mmWave massive MIMO systems with HBF reception. The proposed iterative algorithm exploits jointly the low rank and beamspace sparsity properties to provide more accurate recovery, especially for short beam training intervals. We introduce a novel analog combining architecture that includes a random sub- sampling step before the input of the analog received signals to the digital component of the HBF receiver. This step supports the proposed estimation technique in providing the sampling set of measurements for recovering the unknown channel matrix. The impact of various system parameters on the effectiveness of the designed algorithm and the performance improvement of our technique over representative state-of-the-art ones is demonstrated through indicative simulation results.

Many mixed reality applications are b ased on the real-time compression and streaming of three-dimensional (3D) mod­ els. Thus, they demand very high- bandwidth and ultra-low latency from network specifications. The next-generation wireless networks will employ promising technologies to sig­ nificantly improve the communication data rates. However, due to implementation complexity and thus increased en­ ergy consumption of these technologies, a trade-off between the quality-of-use r-experience (QoE) and the hardware spec­ ifications is necessary. To overcome these limitations low- resolution quantizers have been of inte rest, which provide a trade-off between quality and complexity. In this p aper, we propose a complexity-aware perceptual coding scheme that minimizes the reconstruction losses of the 3D models. Ex­ tensive simulations assuming different 3D models show that the proposed scheme achieves plausible reconstruction output offering significantly higher energy efficiency gains, as com­ pared to a context unaware coding approaches.

Low resolution analog-to-digital converters (ADCs) can be employed to improve the energy efficiency (EE) of a wireless receiver since the power consumption of each ADC is exponentially related to its sampling resolution and the hardware complexity. In this paper , we aim to jointly optimize the sampling resolution, i.e., the number of ADC bits, and analog/digital hybrid combiner matrices which provides highly energy efficient solutions for millimeter wave multiple-input multiple-output systems. A novel decomposition of the hybrid combiner to three parts is introduced: the analog combiner matrix, the bit resolution matrix and the baseband combiner matrix. The unknown matrices are computed as the solution to a matrix factorization problem where the optimal, fully digital combiner is approximated by the product of these matrices. An efficient solution based on the alternating direction method of multipliers is proposed to solve this problem. The simulation results show that the proposed solution achieves high EE performance when compared with existing benchmark techniques that use fixed ADC resolutions.

This paper proposes an energy efficient millimeter wave (mmW ave) hybrid multiple-input multiple-output (MIMO) beamformer with low resolution digital to analog converters (DACs) at the transmitter . W e consider the case where all DACs have the same sampling resolution for each radio frequency (RF) chain and select the best subset of the active RF chains and the DAC resolution. A novel technique based on the Dinkelbach method and subset selection optimization is proposed to maximize the energy efficiency (EE) given a predefined power budget for transmission. W e also implement an exhaustive search approach to serve as an upper bound on the EE performance and show the performance trade-offs. The simulation results verify that the proposed technique exhibits EE performance similar to the optimal exhaustive search technique while requiring lower computational complexity . Index T erms—energy efficiency maximization, low resolution DACs, mmW ave MIMO, hybrid beamforming. I. I N T RO D U C T I O N Millimeter W ave (mmW ave) technology can meet the needs of the fifth generation (5G) wireless communication systems and provide improved rate and capacity [1], [2].

This paper proposes a novel architecture with a framework that dynamically activates the optimal number of radio frequency (RF) chains used to implement hybrid beam- forming in a millimeter wave (mmWave) multiple-input and multiple-output (MIMO) system. We use fractional programming to solve an energy efficiency maximization problem and exploit the Dinkelbach method (DM)-based framework to optimize the number of active RF chains and data streams. This solution is updated dynamically based on the current channel conditions, where the analog/digital (A/D) hybrid precoder and combiner matrices at the transmitter and the receiver, respectively, are designed using a codebook-based fast approximation solution called gradient pursuit (GP). The GP algorithm shows less run time and complexity while compared to the state-of-the- art orthogonal matching pursuit (OMP) solution. The energy and spectral efficiency performance of the proposed frame- work is compared with the existing state-of-the-art solutions, such as the brute force (BF), the digital beamformer, and the analog beamformer. The codebook-free approaches to design the precoders and combiners, such as alternating direction method of multipliers (ADMMs) and singular value decompo- sition (SVD)-based solution are also shown to be incorporated into the proposed framework to achieve better energy efficiency performance.

Millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems realizing directive beamforming require reliable estimation of the wireless propagation channel. However, mmWave channels are characterized by high variability that severely challenges their recovery over short training periods. Current channel estimation techniques exploit either the channel sparsity in the beamspace domain or its low-rank property in the antenna domain, nevertheless, they still require large numbers of training symbols for the satisfactory performance. In this letter, we present a novel channel estimation algorithm that jointly exploits the latter two properties of mmWave channels to provide more accurate recovery, especially for shorter training intervals. The proposed iterative algorithm is based on the alternating direction method of multipliers and provides the global optimum solution to the considered convex mmWave channel estimation problem with fast convergence properties. Index T erms —Alternating direction method of multiplier (ADMM), beamforming, channel estimation, massive multiple in- put multiple output (MIMO), matrix completion, millimeter wave. I.

Millimeter wave (mmWave) multiple-input multiple- output (MIMO) systems have recently been proposed to meet the needs of the future wireless communication standards. The efficient use of low resolution digital-to-analog converters (DACs) and hybrid architecture could significantly reduce the high power consumption associated with the mmWave MIMO system components. This paper designs an energy efficient transmitter with low resolution DACs for mmWave massive MIMO systems. An optimization problem is formulated and solved to find the optimal number of radio-frequency (RF) chains to be used at the transmitter to minimize the power consumption. This problem is constrained by the information loss which introduces the reduction of the number of the RF chains, expressed in terms of the system capacity. Rate and energy performance are compared with different beamforming techniques and architectures for various DAC resolutions.

This paper proposes an efficient channel estima- tion algorithm for millimeter wave (mmWave) systems with a hybrid analog-digital multiple-input multiple-output (MIMO) architecture and few-bits quantization at the receiver. The sparsity of the mmWave MIMO channel is exploited for the problem formulation while limited resolution analog-to-digital converters (ADCs) are used in the receiver architecture. The estimation problem can be tackled using compressed sensing through the Stein’s unbiased risk estimate (SURE) based parametric denoiser with the generalized approximate message passing (GAMP) framework. Expectation-maximization (EM) density estimation is used to avoid the need of specifying channel statistics resulting the EM-SURE-GAMP algorithm to estimate the channel. SURE, depending on the noisy observation, is mini- mized to adaptively optimize the denoiser within the parametric class at each iteration. The proposed solution is compared with the expectation-maximization generalized AMP (EM-GAMP) solution and the mean square error (MSE) performs better with respect to low and high signal-to-noise ratio (SNR) regimes, the number of ADC bits, and the training length. The use of the low resolution ADCs reduces power consumption and leads to an efficient mmWave MIMO system.

In this work we consider the challenging problem of chan- nel estimation at the receiver of a massive multiple-input multiple-output system with hybrid analog/digital beamform- ing and low-resolution quantization. We propose a dithered beamforming architecture, where random control signals are injected to the analog part of the receiver beamformer and to the analog-to-digital converters to introduce randomness into the signal capturing process and combat the stair-case quanti- zation effects. The statistical properties of the dithered output are captured via an Expectation-Maximization approximation of the maximum a-posteriori estimator. A low-complexity al- gorithm is proposed which exhibits performance close to the oracle-based least-squares estimation of the sparse channel.

Recently, there has been increasing interest for easy and reliable generation of 3-D animated models facilitating several real-time applications (like immersive telepresence, motion capture, and gaming). In most of these applications, the reconstruction of soft body animations is based on time- varying point clouds, which are nonuniformly sampled and highly incomplete. To overcome these significantly challenging imperfections without any additional information, first we introduce a novel reconstruction technique based on rank minimization theory, which can result in a unique solution to the otherwise ill-posed problem. This technique is further extended to exploit the spatial coherence, which usually characterizes the soft-body animations. Based on the developed tools, we propose a distributed consolidation technique where the reconstruction is performed by working simultaneously on several groups of frames. To achieve this, we impose temporal coherence between successive frame clusters by constraining the rank minimization problem.

The performance of orthogonal frequency division multiplexing systems in vehicular environments suffers from intercarrier interference (ICI) and the inherent non station- arity of the channel statistics. Receiver windowing constitutes an effective technique for enhancing the banded structure of the frequency-domain channel matrix, thus improving the effec- tiveness of a banded equalizer for ICI mitigation. However, its optimality has been verified only for stationary channels with perfectly known statistics. In non stationary channels, the second-order statistics have to be tracked and the optimal per- formance can be achieved at the expense of cubic complexity over the number of the subcarriers. To overcome this limita- tion, an adaptive windowing technique is proposed that is able to track directly an optimal receiver window in terms of average signal-to-interference noise ratio, requiring only linear complex- ity. Extensive simulation results verify both the ability of the proposed approach to track the time varying channel statistics and its increased robustness to channel estimation errors that are common in vehicular environments.

With the growing demand for easy and reliable generation of 3D models representing real-world or synthetic objects, new schemes for acquisition, storage, and transmission of 3D meshes are required. In principle, 3D meshes consist of vertex positions and vertex connectivity. Vertex position encoders are much more resource demanding than connectivity encoders, stressing the need for novel geometry compression schemes. The design of an accurate and efficient geometry compression system can be achieved by increasing the compression ratio without affecting the visual quality of the object and minimizing the computational complexity. In this paper, we present novel compression/reconstruction schemes that enable aggressive compression ratios, without significantly reducing the visual quality. The encoding is performed by simply executing additions/subtractions. The benefits of the proposed method become more apparent as the density of the meshes increases, while it provides a flexible framework to trade efficiency for reconstruction quality.

Automotive industry will be greatly benefited by the advent of 5G Networking and the huge boost in performance and coverage it will support. Road safety and traffic efficiency services will be significantly upgraded through seamlessly interconnected devices, while latency decrease will most likely allow autonomous driving to become a commodity, available to everyone. This technology will have a huge societal and economical impact, since it will render severe traffic accidents, long commute times and increased energy consumption obsolete. Current vehicular communication systems are usually equipped with orthogonal frequency division multiplexing (OFDM) transceivers that op- erate in suboptimal modes for the upcoming 5G standards. The problem originates in the existing intercarrier interference (ICI) on the receiver end, often partially tackled by integrated successive interference cancellation (OSIC) architectures. How- ever, for decreasing complexity, system designers attempt to mitigate ICI by considering only a small number or sub-carriers, leading to error floor

In this paper , a supervised energy disaggregation method is proposed. The appliances that are monitored, are modelled by multi-state finite state machines. Each state of an appliance is described by exactly one vector of power consumptions from a carefully designed set of such vectors (called atoms), that comprise a dictionary. The latter is constructed during a training phase, where it is assumed that individual power consumption signals are available. A clustering algorithm is applied on overlapping patches extracted from the training signals to select a fixed number of representatives, i.e., the atoms of the dictionary. Moreover , in the training phase, an appropriate state transition probabilities matrix is constructed. During the operation phase, where the actual disaggregation task is performed, a trellis, with a reduced number of transitions, is used for the acquisition of the disaggregated power consumption signals per appliance. Numerical results, using the REDD dataset, are provided, in order to demonstrate the effectiveness of the proposed method. I.

Vehicular communication systems are usually equipped with orthogonal frequency division multiplexing (OFDM) transceivers that operate on rapidly changing radio propagation environments, which results in high Doppler and delay spreads. More specifically, in these environments, the experienced channels are doubly selective and introduce severe intercarrier interference (ICI) at the receiver. An effective ICI mitigation technique is desired as a constituent part of an ordered successive interference cancellation (OSIC) architecture, which turns out to be computationally efficient, since it may require the solution of linear systems with multiple right-hand sides. To decrease the complexity, several techniques suggest mitigating the ICI by considering only a small number of adjacent subcarriers. However, this approximation introduces an error floor, which may result in unacceptable bit error rates (BER) at high signal-to-noise ratio regimes. In this paper, we propose a new OSIC equalization technique based on an iterative Galerkin projection-based algorithm that reduces the computational cost without sacrificing the performance gains of the OSIC architecture.

In this paper, we consider the problem of distributed beamforming for maximization of the receiver signal-to-noise­ ratio (SNR) subject to a total transmit power constraint. We investigate the case where the optimal beamforming weights are expressed based on the second-order statistics of the in­ volved channels, while the communication among the relays is interference-limited. In this context, we propose a relay­ cooperative scheme for interference minimization, where only a limited number of correlation quantities are sent to the fusion center (FC). We propose a technique which overcomes the problem of the incomplete covariance matrices via matrix completion. Through simulation results, we show that, after a number of iterations, the proposed technique converges to the true covariance matrices and thus the optimal beamformer may be computed.

The correlation structure among the sensor observa- tions is a significant characteristic of the wireless sensor network (WSN) which can be exploited to drastically enhance the overall network performance. This structure is usually expressed as a low-rank approximation of the correlation matrix, although, in many cases the correlation of the captured data is full- rank. Thus, the computation of the full-rank correlation matrix by centralizing all the measurements into one node, puts at risk the privacy of the WSN. To overcome this problem, we impose privacy-preserving restrictions, in order to constrain the cooperation among the nodes, and hence promote the privacy. To this end, the decentralized estimation of the network-wide corre- lation matrix is obtained via a novel adaptive matrix completion technique, where at each step, a rank-one completion problem is solved. Through simulation experiments it has been verified that proposed algorithm converges to the full rank correlation matrix. Moreover, the proposed algorithm exhibits significantly lower computational complexity than the conventional technique. I.

In this work, we consider a wireless OFDM system operating over doubly selective channels, where the Doppler effect destroys the orthogonality between subcarriers and hence, results into severe intercarrier interference (ICI). To mitigate this effect, computational demanding equalization schemes that require the inversion of the channel matrix, should be applied. In order to achieve linear complexity in the number of the subcarriers, a banded approximation of the channel matrix is usually adopted, whereas the performance of the equalizer is sig­ nificantly degraded. To recover this performance loss, we propose a regularized estimation framework for MMSE ICI equalization in the frequency domain, where the complexity remains linear with respect to the number of the subcarriers. Simulation results verify the effectiveness of the proposed regularization. I. INTROD UCTION Orthogonal frequency-division multiplexing (OFDM) has been used for several wireless applications such as broadcast­ ing of digital audio and digital video [1], [2] and mobile radio communication [3], [4].

Doubly selective channels can cause severe perfor- mance degradation in orthogonal frequency division multiplexing (OFDM) systems, introducing inter-carrier interference (ICI) at the receiver. In such cases, equalization schemes which require matrix inversion are prohibitively complex for large OFDM symbol lengths. In this paper, we propose two low-complexity iterative successive interference cancellation schemes, applying Krylov subspace optimization methods. We first derive a reduced- rank preconditioned conjugate gradient (PCG) algorithm in order to estimate the equalization matrix with a reduced number of iterations. We then develop an improved PCG algorithm with the same complexity order, using the Galerkin projections theory. As verified via simulations, the proposed schemes may offer near optimal performance with reduced computational complexity.

Multi-cell coordinated beamforming (CB) can mit- igate inter-cell interference. However, previous study on CB focuses on systems with only one receive antenna. This paper considers CB for systems with multiple receive antennas. To take fairness among scheduled users into account, CB is designed to maximize the harmonic sum of signal-to-interference-plus- noise ratio (SINR). We develop an iterative algorithm that can guarantee convergence. Simulation shows that the proposed algorithm have 70% and 47% throughput gains over single cell beamforming for 10th percentile user throughput and median user throughput, respectively. I. I NTRODUCTION In order to increase the capacity of cellular networks, dense cells can be deployed. For dense cellular networks, the performance is limited by inter-cell interference (ICI). Thus, mitigating ICI has become an important issue. Traditional methods of mitigating ICI is to optimize the transmit power of the cells in time or frequency domain to improve performance of the cell-edge users [1], [2]. Nowadays, improvement in backhaul connection allowed a large amount of information to be shared among base stations (BS) quickly.

This paper addresses the problem of noise removal in X-ray medical images. A novel scheme for image denoising is proposed, by leveraging recent advances in sparse and redundant representations. The noisy X-ray image is decomposed, with respect to an overcomplete dictionary which is either fixed or trained on the noisy image, and it is reconstructed using greedy techniques. The new scheme has been tested with both artificial and real X-ray images and it turns out that it may offer superior denoising results as compared to other existing methods.

Distributed generation and the urge for a more effi- cient grid operation will increase the frequency of network topol- ogy reconfigurations in tomorrow’s power grids. High-throughput synchrophasor and intelligent electronic device readings provide unprecedented instrumentation capabilities for generalized state estimation (GSE), which deals with identifying the power system state jointly with its network topology. This task is critically challenged by the complexity scale of a grid interconnection, especially under the detailed GSE model. Upon modifying the original GSE cost by block-sparsity promoting regularizers, a decentralized solver with enhanced circuit breaker verification capabilities is developed. Built on the alternating direction method of multipliers, the novel method maintains compatibility with existing solvers and requires minimum information exchanges across the control centers of neighboring power grids. Numer- ical tests on an extended IEEE 14-bus model corroborate the effectiveness of the novel approach. I. I NTRODUCTION Network topology cognition and state estimation are two basic modules in power system monitoring [1].

In this paper, a new heuristic algorithm for the sparse adaptive equalization problem, termed as stochastic gradient pursuit, is proposed. A decision-feedback equalization structure is used in order to effectively mitigate the effect of long mul- tipath channels. Diverging from the commonly used approach of sparse channel identification, we exploit the sparsity of the inverse problem under the compressive sensing perspective. Also, an extension to the case where the sparsity order parameter is unknown, is developed. Simulation results verify that the pro- posed schemes exhibit faster convergence and improved tracking capabilities compared to conventional and other sparse aware equalization schemes, offering at the same time a reduced compu- tational complexity.

In this paper new efficient decision feedback equal- ization (DFE) schemes for channels with long and sparse im- pulse responses are proposed. It has been shown that under reasonable assumptions concerning the channel impulse response (CIR) coefficients, the feedforward (FF) and feedback (FB) filters may be also approximated by sparse filters. Either the sparsity of the CIR, or the sparsity of the DFE filters may be exploited to derive efficient implementations of the DFE. To this end, compressed sampling (CS) approaches, already successful in system identification settings, can significantly improve the performance of the non sparsity aware DFE. Building on basis pursuit and matching pursuit techniques new DFE schemes are proposed that exhibit considerable computational savings, increased performance properties and short training sequence requirements. To investigate the performance of the proposed schemes the restricted isometry property in the common DFE setup is also investigated. I. I NTRODUCTION In high-speed wireless communications, the involved multi- path channels are typically sparse, i.e.

In this paper we propose two new adaptive decision feedback equalization (DFE) schemes for channels with long and sparse impulse responses. It has been shown that for a class of channels, and under reasonable assumptions concerning the DFE filter sizes, the feedforward (FF) and feedback (FB) filters possess also a sparse form. The sparsity form of both the channel impulse response (CIR) and the equalizer filters is properly exploited and two novel adaptive greedy schemes are derived. The first scheme is a channel estimation based one. In this scheme, the non-negligible taps of the involved CIR are first estimated via a new greedy algorithm, and then the FF and FB filters are adaptively computed by exploiting a useful relation between these filters and the CIR. The channel estimation part of this new technique is based on the steepest descent (SD) method and offers considerably improved performance as compared to other adaptive greedy algorithms that have been proposed. The second scheme is a direct adaptive sparse equalizer based on a SD-based greedy algorithm.