We consider the downlink of a massive multiple-input-multiple-output (mMIMO) system, where a base station (BS) serves multiple clusters of single-antenna user equipments (UEs) by using multiple single-antenna relays. The BS employs a novel rate-splitting multiple access (RSMA) private interference cancellation (PIC) protocol wherein, similar to RSMA, a UE first decodes its common message. To decode its private message, a UE then cancels interference not only from the common message but, unlike RSMA, also from the private messages of weaker UEs in its own cluster. The RSMA-PIC protocol integrates the benefits of non-orthogonal multiple access (NOMA) and RSMA protocols, and outperforms them in low and high signal-to-noise regime. The BS employs minimum-mean-squared-error precoder to cancel the inter-relay interference between multiple relays. We also develop a decentralized two layer iterative optimization framework to maximize the weighted sum energy efficiency metric.

We consider a downlink multi-cell massive multi-input-multi-output system, where the base station (BS) in each cell serves its user equipments (UEs) via a cell-specific intelligent reflecting surface (IRS). The BS, IRS and UEs have dual-polarized antennas. We derive a lower bound on the spectral efficiency (SE) of this system, which operates in practical spatially-correlated Rician-fading channels. We also maximize the system global energy efficiency by optimizing the BS transmit powers and IRS phases. We optimize the transmit powers by designing a low-complexity minorization-maximization algorithm, which provides a closed form solution. We then optimize the IRS phases by using a multi-agent deep deterministic policy gradient method, where each BS learns optimal phases by sharing only the policies with each other, and that too without any supervision. We numerically show i) the BS-UE link attenuation for which an IRS can be replaced with dual-polarized antennas at the BS and UEs; and ii) that dual-polarized antennas enable a multicell IRS system to match its SE with a single-cell system with single-polarized antennas at the BS, IRS and UEs.

We consider the downlink of a massive multi-input-multi-output (mMIMO) system where a base station (BS) serves ultra-reliable and low-latency communication (URLLC) user equipments (UEs) by employing rate-splitting multiple access (RSMA) technology. The UEs are assumed to be mobile, and their channels consequently age. The channels are spatially-correlated and Rician faded, with a phase-shifted line-of-sight (LoS) component, and the BS employs maximum ratio (MR) and minimum mean squared error (MMSE) precoders. A closed-form spectral efficiency (SE) expression is derived for the MR precoder, which is then used to develop a novel low-complexity optimal power allocation algorithm for the BS to maximize the global energy efficiency (GEE) metric. For the MMSE precoder, whose closed-form SE expression is difficult to derive, an iterative stochastic optimization algorithm is proposed. This algorithm, which is designed to have closed-form power update expressions, converges faster to the optimal solution than a conventional technique, which numerically calculates the SE. These low-complexity practically-implementable algorithms are developed by applying the epigraph, Lagrangian dual and quadratic transformations. We numerically show that RSMA outperforms spatial division multiple access when channels age faster, have a low Rician factor, and have a low correlation.

We consider a practical hardware-impaired multi-cell massive multi-input multi-output (mMIMO) system with spatially-correlated Rician fading channels, whose line-of-sight component experiences a random phase-shift. The multi -cell mMIMO system employs two-layer large-scale fading decoding (LSFD) to mitigate the interference due to pilot contamination. We consider a dynamic analog-to-digital converter (ADC) architecture at the base stations (BSs), which enables us to independently vary the resolution of ADC connected to each of its antenna, and thus properly choose them to achieve a high spectral efficiency (SE). Both BS and user equipments (UEs) are also equipped with low-cost radio frequency chains, which introduce additional hardware impairments. We first derive a closed-form SE expression for this system, and then optimize the LSFD vectors to maximize the sum SE metric using generalized Rayleigh quotient. We investigate the ADC resolution and hardware impairments values for which LSFD is highly effective, and the values for which its effectiveness reduces.

We consider the downlink of massive multi-input-multi-output (mMIMO) rate-splitting multiple-access (RSMA) system with spatially-correlated Rician channels that experience phase shifts in their line-of-sight (LoS) component. We also assume that channel ages due to highly mobile users. We estimate the channels at the beginning of the coherence block, and use it to design the precoders using maximum ratio (MR) technique in the data transmission phase. We derive a closed-form spectral efficiency (SE) expressions for the above system, and numerically validate its correctness by comparing with the ergodic SE. We investigate the SE gain provided by RSMA over its conventional spatial-division multiple access (SDMA) counterpart. We crucially show only at lower Rician factor, which indicates the strength of LoS component over its non-LoS (NLoS) counterpart, RSMA has a higher SE than SDMA. For higherRician factor, SDMA has a higher SE than RSMA. We also show that the SE gain of RSMA over SDMA increases with increase in channel aging.

We consider a two-way full-duplex (FD) system where a massive multi-input-multi-output FD base station (BS) communicates with multiple FD users via an intelligent reflecting surface (IRS). We derive a closed-form network spectral efficiency lower bound by considering spatial correlation at the BS and IRS. We demonstrate the importance of modelling spatial correlation by simplifying this lower bound for uncorrelated channels to show that the IRS capability to modify the wireless medium now is significantly impeded. This lower bound, which is a function of only the long-term channel statistics, is also used to maximize the non-concave global energy efficiency metric by optimally allocating the transmit powers, and by designing the IRS phases. We derive closed-form updates for optimizing the transmit powers using Lagrangian dual, Quadratic, and Dinkelbach’s transforms. We then optimize the IRS phases by using the projected gradient ascent algorithm. We numerically show that increasing the number of IRS elements can help a FD mMIMO BS outperform its half-duplex counterpart, which otherwise under-performs due to its limited ability to cancel various FD interferences.

We consider a full duplex (FD) massive multiple-input multiple-output (mMIMO) cell-free (CF) system, where a large number of multi-antenna FD access points (APs) jointly serve multiple FD user equipments (UEs). The APs transmit precoded downlink pilots for the UEs to estimate the effective downlink channel. For this system, we derive closed-form uplink and downlink spectral efficiency (SE) expressions by considering i) radio-frequency (RF) impairments at the APs and UEs; ii) dynamic resolution analog-to-digital converter/digital-to-analog converter (ADC/DAC) architecture at the APs and low-resolution ADC/DACs at the UEs; and iii) spatially-correlated Rician channels. We then maximize the non-convex global energy efficiency metric by using the block minorization-maximization technique, which decomposes the main optimization into multiple convex surrogate sub-problems. We analytically show that the SE gain obtained with downlink training is limited for practical CF systems with RF and ADC/DAC impairments, Rician channel and pilot contamination. We also extensively investigate the impact of FD interferences on the downlink training gain.

We consider a multi-user system where a massive multi-input-multi-output (mMIMO) full-duplex (FD) base-station (BS) communicates with multiple FD users via an intelligent reflecting surface (IRS). We derive novel uplink and downlink spectral efficiency (SE) lower bound expressions when the BS estimates composite BS-IRS-user channels. The lower bounds are derived considering spatially-correlated IRS and user channels, and require only statistical channel state information (CSI). We propose a projected gradient ascent based IRS phase optimization algorithm, which also uses only statistical CSI, and enables the system to achieve a higher SE in the presence of the loop and inter-user interferences. We analytically investigate the dependence of SE on the IRS phase for spatially correlated channels considered herein. We show that the proposed analysis can help in increasing the SE by appropriate IRS placement.

We consider two-way multi-pair full-duplex (FD) massive multi-input-multi-output (mMIMO) relaying, where multiple FD user-pairs exchange information via a shared FD relay. We derive a closed-form spectral efficiency (SE) lower bound by considering i) dynamic analog-to-digital converter (ADC) and digital-to-analog converter (DAC) architecture at the relay, where each antenna is connected to a different resolution ADC/DAC; and ii) low cost radio frequency (RF) chains at the relay and users. The dynamic ADC/DAC architecture allows us to judiciously choose ADC/DAC resolution to achieve high SE with low power consumption. We unlike, the most existing mMIMO relaying works, consider a correlated Rician fading channel, which captures realistic line-of-sight (LoS) signal propagation and spatial correlation between closely-packed relay antennas. We maximize the derived SE lower bound by first proposing its valid surrogate function, and then by using minorization-maximization approach. We investigate the impact of ADC/DAC and RF impairments, and show that the proposed optimization allows reduction in ADC/DAC resolution without compromising the SE.

Most of the existing spectral efficiency (SE) investigations for massive multi-input multi-output (MIMO) relaying assume that each antenna has a dedicated radio-frequency (RF) chain with high-quality user and relay hardware. We consider a hybrid massive MIMO multi-pair two-way half-duplex relay, wherein each RF chain steers multiple antennas and assume that both users and relay have hardware impairments. We derive i) a novel closed-form SE expression using large-scale approximation; and ii) asymptotic SE expressions for two different power scaling schemes. We numerically demonstrate that i) a hardware-impaired hybrid relay has measurably degraded SE than a hardware-impaired conventional full-RF chain relay; and ii) the impact of hardware impairments do not vanish asymptotically as N → ∞, mainly due to users hardware impairments.

We consider multipair two-way full-duplex massive multiple-input multiple-output (mMIMO) hardware- impaired relay which facilitates single-antenna full-duplex users to exchange information. The key to cost-efficient implementation of mMIMO relays is to use low cost relay hardware which are prone to impairments. These impairments however, degrade the system performance. The existing literature models the relay hardware impairments as additive noise, by ignoring the multiplicative phase noise. In this paper, we study the impact of multiplicative phase noise along with the residual hardware impairments and receiver noise at the relay. We derive a closed form spectral efficiency (SE) lower bound with maximum ratio combining/maximum ratio transmission at the relay, which is applicable for arbitrary number of relay antennas. We also numerically validate the tightness of the derived closed-form SE expression with the ergodic SE expression and analyze the impact of phase noise along with additive hardware impairments on SE.