Wireless Energy Harvesting For Autonomous Reconfigurable Intelligent Surfaces

in IEEE Transactions on Green Communications and Networking (in press)

Intelligent Reflecting Surface-assisted MU-MISOSystems with Imperfect Hardware: ChannelEstimation and Beamforming Design

in IEEE Transactions on Wireless Communications (2022), 21(3), 2077-2092

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improvingthe spectral and energy efficiency of the fifth-generation (5G)and beyond networks. It ... [more ▼]

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improvingthe spectral and energy efficiency of the fifth-generation (5G)and beyond networks. It is also noteworthy that an IRS canshape the reflected signal propagation. Most works in IRS-assisted systems have ignored the impact of the inevitable residualhardware impairments (HWIs) at both the transceiver hardwareand the IRS while any relevant works have addressed only simplescenarios, e.g., with single-antenna network nodes and/or withouttaking the randomness of phase noise at the IRS into account.In this work, we aim at filling up this gap by considering ageneral IRS-assisted multi-user (MU) multiple-input single-output(MISO) system with imperfect channel state information (CSI)and correlated Rayleigh fading. In parallel, we present a generalcomputationally efficient methodology for IRS reflect beamforming(RB) optimization. Specifically, we introduce an advantageouschannel estimation (CE) method for such systems accounting forthe HWIs. Moreover, we derive the uplink achievable spectralefficiency (SE) with maximal-ratio combining (MRC) receiver,displaying three significant advantages being: 1) its closed-formexpression, 2) its dependence only on large-scale statistics, and3) its low training overhead. Notably, by exploiting the first twobenefits, we achieve to perform optimization with respect to thethat can take place only at every several coherence intervals,and thus, reduces significantly the computational cost comparedto other methods which require frequent phase optimization.Among the insightful observations, we highlight that uncorrelatedRayleigh fading does not allow optimization of the SE, whichmakes the application of an IRS ineffective. Also, in the case thatthe phase drifts, describing the distortion of the phases in theRBM, are uniformly distributed, the presence of an IRS providesno advantage. The analytical results outperform previous worksand are verified by Monte-Carlo (MC) simulations. [less ▲]

Joint Spatial Division and Multiplexing for FDD in Intelligent Reflecting Surface-assisted Massive MIMO Systems

in IEEE Transactions on Vehicular Technology (2022)

Towards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems

in IEEE Transactions on Green Communications and Networking (2021), 5(2), 816-831

Motivated by the ever-growing demand for green wireless communications and the advantages of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, we focus on the design of their downlink ... [more ▼]

Motivated by the ever-growing demand for green wireless communications and the advantages of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, we focus on the design of their downlink (DL) for optimal energy efficiency (EE). To address this fundamental topic, we assume that each access point (AP) is deployed with multiple antennas and serves multiple users on the same time-frequency resource while the APs are Poisson point process (PPP) distributed, which approaches realistically their opportunistic spatial randomness. Relied on tools from stochastic geometry, we derive a lower bound on the DL average achievable spectral efficiency (SE). Next, we consider a realistic power consumption model for CF mMIMO systems. These steps enable the formulation of a tractable optimization problem concerning the DL EE, which results in the analytical determination of the optimal pilot reuse factor, the AP density, and the number of AP antennas and users that maximize the EE. Hence, we provide useful design guidelines for CF mMIMO systems relating to fundamental system variables towards optimal EE. Among the results, we observe that an optimal pilot reuse factor and AP density exist, while larger values result in an increase of the interference, and subsequently, lower EE. Overall, it is shown that the CF mMIMO technology is a promising candidate for next-generation networks achieving simultaneously high SE and EE. [less ▲]

Federated Learning for Physical Layer Design

in IEEE Communications Magazine (2021)

Model-free techniques, such as machine learning (ML), have recently attracted much interest towards the physical layer design, e.g., symbol detection, channel estimation, and beamforming. Most of these ML ... [more ▼]

Model-free techniques, such as machine learning (ML), have recently attracted much interest towards the physical layer design, e.g., symbol detection, channel estimation, and beamforming. Most of these ML techniques employ centralized learning (CL) schemes and assume the availability of datasets at a parameter server (PS), demanding the transmission of data from edge devices, such as mobile phones, to the PS. Exploiting the data generated at the edge, federated learning (FL) has been proposed recently as a distributed learning scheme, in which each device computes the model parameters and sends them to the PS for model aggregation while the datasets are kept intact at the edge. Thus, FL is more communication-efficient and privacy-preserving than CL and applicable to the wireless communication scenarios, wherein the data are generated at the edge devices. This article presents the recent advances in FL-based training for physical layer design problems. Compared to CL, the effectiveness of FL is presented in terms of communication overhead with a slight performance loss in the learning accuracy. The design challenges, such as model, data, and hardware complexity, are also discussed in detail along with possible solutions. [less ▲]

Scalable Cell-Free Massive MIMO Systems: Impact of Hardware Impairments

in IEEE Transactions on Vehicular Technology (2021), 70(10), 9701-9715

Scalable cell-free CF (SCF) massive multiple-input-multiple-output (mMIMO) systems is a promising technology to cover the demands for higher data rates and increasing number of users in fifth generation ... [more ▼]

Scalable cell-free CF (SCF) massive multiple-input-multiple-output (mMIMO) systems is a promising technology to cover the demands for higher data rates and increasing number of users in fifth generation (5G) networks and beyond. According to this concept, a large number of distributed access points (APs) communicates with the users in the network by means of joint coherent transmission while facing the main challenges against standard CF mMIMO systems being their high fronthaul load and computational complexity. Given that the cost-efficient deployment of such large networks requires low-cost transceivers being prone to unavoidable hardware imperfections, in this work, we focus on their impact on the advantageous SCF mMIMO systems by means of a general model accounting for both additive and multiplicative hardware impairments (HWIs). Notably, the scalability, depending on the time-variant characteristics of the network, is clearly affected by means of HWIs being time-varying. There is no other work in the literature studying the phase noise (PN) in CF mMIMO systems or in general any HWIs in SCF mMIMO systems. Hence, we derive upper and lower bounds on the uplink capacity accounting for HWIs. Especially, the lower bound is derived in closed-form by means of the theory of deterministic equivalents (DEs) and after obtaining the optimal hardware-aware (HA) partial minimum mean-squared error (PMMSE) combiner. Among the interesting findings, we observe that separate local oscillators (SLOs) outperform a common LO (CLO) architecture, and the additive transmit distortion degrades more the performance than the receive distortion. [less ▲]

Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

in IEEE Wireless Communications Letters (2020), 9(Sept. 2020), 1447-1451

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A ... [more ▼]

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated. [less ▲]

Impact of residual transceiver impairments on MMSE filtering performance of Rayleigh-product MIMO channels

in Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2017) (2017, July)

ecent studies have demonstrated the presence of residual transceiver hardware impairments even after employing calibration and compensation techniques in different wireless systems. The effect of these ... [more ▼]

ecent studies have demonstrated the presence of residual transceiver hardware impairments even after employing calibration and compensation techniques in different wireless systems. The effect of these impairments becomes more severe in the systems involving a large number of inexpensive Radio Frequency (RF) chains such as massive Multiple Input Multiple Output (MIMO) systems due to the requirement of cost-efficient implementation. However, most of the existing studies consider ideal transceivers without incorporating the effect of residual hardware impairments. In this regard, this paper studies the impact of additive residual transceiver hardware impairments on the Minimum Mean Square Error (MMSE) filtering performance of Rayleigh-Product (RP) MIMO channels. Using principles from Random Matrix Theory (RMT), the MMSE filtering performance of the RP channels is analyzed and a tight lower bound is derived by taking the effects of residual additive transceiver impairments into account. Moreover, some useful insights on the performance of the considered system with respect to various parameters such as the transmit Signal to Noise Ratio (SNR), the number of scatterers and the severity of impairments on both the transmit and receive sides are provided. [less ▲]

Impact of Transceiver Hardware Impairments on the Ergodic Channel Capacity for Rayleigh-Product MIMO Channels

in Proceedings of IEEE SPAWC 2016 (2016, July)

This paper aims at a realistic evaluation of Rayleighproduct multiple-input multiple-output (MIMO) systems. Specifically, by considering the residual transceiver hardware impairments into account, we ... [more ▼]

This paper aims at a realistic evaluation of Rayleighproduct multiple-input multiple-output (MIMO) systems. Specifically, by considering the residual transceiver hardware impairments into account, we derive the ergodic channel capacity of a MIMO system with optimal receivers in the case of insufficient scattering. Actually, motivated by the increasing interest for massive MIMO systems, we investigate the impact of transceiver hardware imperfections in systems with both finite (conventional) and large number of antennas under rank deficient channel matrix conditions by varying the severity of hardware quality. Among the interesting outcomes, we emphasize that the residual hardware transceiver impairments result to a saturation of the ergodic channel capacity within the high signal-to-noise ratio (SNR) regime. Furthermore, we observe that the higher the “richness” of the scattering environment, the higher the ergodic channel capacity till it gets saturated. [less ▲]

Impact of Transceiver Impairments on the Capacity of Dual-Hop Relay Massive MIMO Systems

in Proceedings of IEEE Globecom 2015 (2015, December)

Despite the deleterious effect of hardware impairments on communication systems, most prior works have not investigated their impact on widely used relay systems. Most importantly, the application of ... [more ▼]

Despite the deleterious effect of hardware impairments on communication systems, most prior works have not investigated their impact on widely used relay systems. Most importantly, the application of inexpensive transceivers, being prone to hardware impairments, is the most cost-efficient way for the implementation of massive multiple-input multiple-output (MIMO) systems. Consequently, the direction of this paper is towards the investigation of the impact of hardware impairments on MIMO relay networks with large number of antennas. Specifically, we obtain the general expression for the ergodic capacity of dual-hop (DH) amplify-and-forward (AF) relay systems. Next, given the advantages of the free probability (FP) theory with comparison to other known techniques in the area of large random matrix theory, we pursue a large limit analysis in terms of number of antennas and users by shedding light to the behavior of relay systems inflicted by hardware impairments. [less ▲]