Impact of User Mobility on Optimal Linear Receivers in Cellular Networks

We consider the uplink of non-cooperative multi-cellular systems deploying multiple antenna elements at the base stations (BS), covering both the cases of conventional and very large number of antennas. Given the inevitable pilot contamination and an arbitrary path-loss for each link, we address the impact of time variation of the channel due to the relative movement between users and BS antennas, which limits system's performance even if the number antennas is increased, as shown. In particular, we propose an optimal linear receiver (OLR) maximizing the received signal-to-interference-plus-noise (SINR). Closed-form lower and upper bounds are derived as well as the deterministic equivalent of the OLR is obtained. Numerical results reveal the outperformance of the proposed OLR against known linear receivers, mostly in environments with high interference and certain user mobility, as well as that massive MIMO is preferable even in time-varying channel conditions.Comment: 3 figures, 6 pages, accepted in ICC 201

Impact of General Channel Aging Conditions on the Downlink Performance of Massive MIMO

© 2016 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Recent works have identified massive multiple-input multiple-output (MIMO) as a key technology for achieving substantial gains in spectral and energy efficiency. Additionally, the turn to low-cost transceivers, being prone to hardware impairments, is the most effective and attractive way for cost-efficient applications concerning massive MIMO systems. In this context, the impact of channel aging, which severely affects performance, is investigated herein by considering a generalized model. Specifically, we show that both Doppler shift due to the users' relative movement, as well as phase noise due to noisy local oscillators, contribute to channel aging. To this end, we first propose a joint model, encompassing both effects, to investigate the performance of a massive MIMO system based on the inevitable time-varying nature of realistic mobile communications. Then, we derive the deterministic equivalents for the signal-to-noise-and-interference ratios (SINRs) with maximum ratio transmission (MRT) and regularized zero-forcing (RZF) precoding. Our analysis not only demonstrates a performance comparison between MRT and RZF under these conditions but, most importantly, also reveals interesting properties regarding the effects of user mobility and phase noise. In particular, the large antenna limit behavior profoundly depends on both effects, but the burden due to user mobility is much more detrimental than phase noise even for moderate user velocities (≈ 30 km/h), whereas the negative impact of phase noise is noteworthy at lower mobility conditions. Moreover, massive MIMO systems are favorable even in general channel aging conditions. Nevertheless, we demonstrate that the transmit power of each user to maintain a certain quality of service can be scaled down, at most, by 1√M (M is the number of base station antennas), which indicates that the joint effects of phase noise and user mobility do not degrade the power scaling law but only the achievable sum-rate.Peer reviewe

Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems : A Deep Learning Approach

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.Peer reviewe

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

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.Comment: 6 pages, 4 figures, accepted in IEEE Global Communications Conference (GLOBECOM 2015) - Workshop on Massive MIMO: From theory to practice, 201

Deterministic equivalent performance analysis of time-varying massive MIMO systems

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Delayed channel state information at the transmitter (CSIT) due to time variation of the channel, coming from the users' relative movement with regard to the BS antennas, is an inevitable degrading performance factor in practical systems. Despite its importance, little attention has been paid to the literature of multi-cellular multiple-input massive multiple-output (MIMO) system by investigating only the maximal ratio combining (MRC) receiver and the maximum ratio transmission (MRT) precoder. Hence, the contribution of this work is designated by the performance analysis/comparison of/with more sophisticated linear techniques, i.e., a minimum-mean-square-error (MMSE) detector for the uplink and a regularized zero-forcing (RZF) precoder for the downlink are assessed. In particular, we derive the deterministic equivalents of the signal-to-interference-plus-noise ratios (SINRs), which capture the effect of delayed CSIT, and make the use of lengthy Monte Carlo simulations unnecessary. Furthermore, prediction of the current CSIT after applying a Wiener filter allows to evaluate the mitigation capabilities of MMSE and RZF. Numerical results depict that the proposed achievable SINRs (MMSE/RZF) are more efficient than simpler solutions (MRC/MRT) in delayed CSIT conditions, and yield a higher prediction at no special computational cost due to their deterministic nature. Nevertheless, it is shown that massive MIMO are preferable even in time-varying channel conditions.Peer reviewe

Performance of massive MIMO uplink with zero-forcing receivers under delayed channels

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we analyze the performance of the uplink communication of massive multicell multiple-input multiple-output (MIMO) systems under the effects of pilot contamination and delayed channels because of terminal mobility. The base stations (BSS) estimate the channels through the uplink training and then use zero-forcing (ZF) processing to decode the transmit signals from the users. The probability density function (pdf) of the signal-to-interference-plus-noise ratio (SINR) is derived for any finite number of antennas. From this pdf, we derive an achievable ergodic rate with a finite number of BS antennas in closed form. Insights into the impact of the Doppler shift (due to terminal mobility) at the low signal-to-noise ratio (SNR) regimes are exposed. In addition, the effects on the outage probability are investigated. Furthermore, the power scaling law and the asymptotic performance result by infinitely increasing the numbers of antennas and terminals (while their ratio is fixed) are provided. The numerical results demonstrate the performance loss for various Doppler shifts. Among the interesting observations revealed is that massive MIMO is favorable even under channel aging conditions.Peer reviewe

Ergodic Capacity Analysis of AF DH MIMO Relay Systems with Residual Transceiver Hardware Impairments : Conventional and Large System Limits

© 2017 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Despite the inevitable presence of transceiver impairments, most prior work on multiple-input multiple-output (MIMO) wireless systems assumes perfect transceiver hardware, which is unrealistic in practice. In this direction, motivated by the increasing interest in MIMO relay systems due to their improved spectral efficiency and coverage, this paper investigates the impact of residual hardware impairments on the ergodic capacity of dual-hop (DH) amplify-and-forward (AF) MIMO relay systems. Specifically, a thorough characterization of the ergodic channel capacity of DH AF relay systems in the presence of hardware impairments is presented herein for both the finite and large antenna regimes by employing results from finite-dimensional and large random matrix theory, respectively. Regarding the former setting, we derive the exact ergodic capacity as well as closed-form expressions for tight upper and lower bounds. Furthermore, we provide an insightful study for the low signal-to-noise ratio regimes. Next, the application of the free probability theory allows us to study the effects of the hardware impairments in future 5G deployments including a large number of antennas. While these results are obtained for the large system limit, simulations show that the asymptotic results are quite precise even for conventional system dimensions.Peer reviewedFinal Accepted Versio