This paper investigates the secrecy performance of a spatially correlated multi-reconfigurable intelligent surface (RIS)-assisted cell-free massive multiple-input multiple-output system in the presence of imperfect channel state information, electromagnetic interference (EMI), and transceiver hardware impairments (T-HWI). Under these imperfections, we derive the linear minimum mean-square error channel estimate and a closed-form expression for the downlink secrecy sum rate (SSR). We employ artificial noise at each access point (AP) in the downlink data transmission phase to improve the achievable SSR. We deduce several analytical insights from the derived closed-form expressions to validate the system performance. We further formulate a joint optimization problem that maximizes the SSR by optimally allocating the power sharing factor and the RIS phase shift matrix. A closed-form solution for the optimal power sharing factor is obtained using a fixed-point equation method, while the RIS phase shifts are designed through a generative diffusion model. Numerical results validate the derived analytical expressions and reveal a substantial degradation in secrecy performance due to the presence of T-HWI and EMI. Notably, the legitimate user-side hardware impairments have a more detrimental effect on the system performance compared to the AP side. Furthermore, a trade-off is observed between the number of RISs and EMI power in achieving optimal SSR. Moreover, simulation results demonstrate that the proposed joint optimization scheme yields substantial gains in achievable SSR over the individual optimization schemes and the random baseline.

This paper investigates the secrecy performance of a spatially correlated multi-reconfigurable intelligent surface (RIS)-assisted cell-free massive multiple-input multiple-output system in the presence of imperfect channel state information, electromagnetic interference (EMI) at each RIS, and transceiver hardware impairments (T-HWI). Under these imperfections, we calculate the linear minimum mean-square error estimation to acquire the channel estimate and derive a closed-form expression for the downlink secrecy sum rate (SSR). We employ artificial noise (AN) at each access point (AP) in the downlink data transmission phase to improve the achievable SSR. Our analysis reveals that, under specific scenarios, hardware distortion at the AP can act as effective AN. Numerical results validate the theoretical analysis and demonstrate that the secrecy performance significantly deteriorates due to the impact of T-HWI and EMI. Furthermore, maintaining the secrecy performance under higher EMI power necessitates increased AN power. Additionally, a performance trade-off exists between the number of RISs and the EMI power for achieving an optimal SSR.

This paper studies a novel architecture of full-duplex (FD) massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) system capitalizing on the synergies between FD to double the spectral efficiency (SE) and massive MIMO ISAC to save on spectrum and hardware resources. In particular, we consider an FD massive MIMO ISAC system where the FD BS simultaneously serves half-duplex (HD) downlink (DL) and uplink (UL) users while performing multiple target sensing. We use communication symbols for sensing through implementation-friendly maximum ratio structured-transmit beamforming (MR-STB). We derive novel closed-form (CF) sum SE expressions for both UL and DL in the presence of imperfect channel state information. Our simulations validate the correctness of the derived CF sum SE. The FD massive MIMO ISAC system achieves a significantly higher gain in spectral efficiency compared to its HD counterparts. The simulation results also demonstrate that the sum SE can be increased by allocating more power to communication without compromising the desired sensing power pattern in the direction of targets measured through the main-to-average sidelobe levels.

This paper proposes a joint optimization framework for active and passive beamforming in reconfigurable intelligent surface (RIS)-aided downlink device-to-device (D2D) communication systems underlaying cellular networks. The proposed framework aims to enhance the weighted sum rate of the system by optimizing active beamforming at the base station (BS) and passive beamforming via the RIS. The optimization problem, formulated as a multivariate and non-convex task, is addressed using advanced techniques, including closed-form approaches such as the Lagrangian dual transform and quadratic fractional programming. This study highlights the potential of RIS technology to reshape propagation environments and improve communication reliability, providing a foundation for efficient and adaptive designs in dense wireless networks. Simulation results demonstrate the effectiveness of the proposed method when compared with benchmark schemes.

This paper investigates the impact of hardware impairments in terms of low-cost radio frequency (RF) chains and dynamic-resolution analog-to-digital/digital-to-analog converters (ADCsIDACs) over a reconfigurable intelligent surface (RIS) enhanced cell-free massive multiple-input multiple-output system. We aim to model a practical scenario by considering phase noise and spatial correlation at the RIS. We utilize the well-known linear minimum mean square error estimation to obtain the channel estimates and present a closed-form expression for the downlink spectral efficiency (SE). To reduce the fronthaul load, we implement an access point (AP) selection strategy that assigns each user to a specific set of APs rather than all the APs. Our analysis reveals that i) the SE with RIS-aided links is significant in scenarios with low transmit power, especially when the direct link is weak, ii) the effect of ADCslDACs imperfection is more acute than RF impairments at higher transmit power levels, iii) the energy efficiency shows minimal impact at lower impairment levels but becomes significantly affected as the impairment level increases, and iv) dynamic-resolution ADCslDACs playa vital role on the system performance.

This paper incorporates a low-complexity precoding scheme, termed partial zero-forcing (PZF), for an orthogonal frequency time space (OTFS) modulated cell-free massive multiple input multiple output (mMIMO) integrated sensing and communication (ISAC) system. This scheme assigns ZF precoding and maximal ratio transmission (MRT) to communication users based on their channel gain. This approach addresses the computational complexity of global ZF operations and the inability of MRT precoding to mitigate inter-user interference. In addition, we introduce the formulation of the mean-to-average-sidelobe-ratio (MASR) metric for sensing within this system. We derive novel closed-form spectral efficiency (SE) expressions and compute the MASR metric to evaluate sensing performance. The results demonstrate that PZF outperforms MRT and full-pilot ZF (FZF) in terms of SE and computational complexity, providing a scalable solution for high mobility beyond 5G applications. We also evaluate how downlink power allocation at the AP influences communication and sensing performance, using closed-form SE per user and MASR as metrics.

We consider a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (mMIMO) integrated sensing and communication (ISAC) system, accounting for both direct and indirect paths to serve the communication users. We practically model the system by employing hybrid partial zero forcing (PZF) precoding to achieve a balance between system performance and computational complexity. We derive the closedform expressions for the achievable sum-rate and mean-to-average sidelobe ratio (MASR) as the communication and sensing metric for the proposed system, respectively. We numerically analyze the impact of STAR-RIS on the trade-off between sensing and communication performance. Additionally, we investigate the performance of PZF precoding for the proposed system.

The physical layer in a cellular network base station typically runs on a field programmable gate array (FPGA) to enable reconfigurability and adaptability to meet future demands of the network with sufficient computational power. This work proposes a novel architecture and algorithms for the FPGA implementation of the fifth-generation (5G) new radio (NR) physical downlink control channel (PDCCH) using the 3GPP procedures for downlink control information (DCI) processing, including sub-block interleaver, bit selection, scrambling, golden sequence generation, and modulation. The proposed algorithms are optimized to meet 5G-NR frame timings for DCI processing with minimum power consumption and hardware resource utilization. We perform rigorous testing including all corner cases to benchmark our designs in i) standalone mode, and ii) integrated fashion for the end-to-end PDCCH processing. The PDCCH implementation, with maximum DCI payload, using the proposed architecture and algorithms achieves$1.5 \mu s$latency,$1.98\%$hardware resource utilization,$1.2$Gbps throughput and$10$Gbps/W power efficiency. With a subcarrier spacing$\Delta f=30$KHz, it can multiplex DCI to schedule up to$20$and$40$users for one orthogonal frequency division multiplexing (OFDM) and two OFDM symbols long PDCCH respectively.

We consider a reconfigurable intelligent surface (RIS) assisted cell-free massive multiple-input multiple-output non-orthogonal multiple access (NOMA) system, where each access point (AP) serves all the users with the aid of the RIS. We practically model the system by considering imperfect instantaneous channel state information (CSI) and employing imperfect successive interference cancellation at the users’ end. We first obtain the channel estimates using linear minimum mean square error approach considering the spatial correlation at the RIS and then derive a closed-form downlink spectral efficiency (SE) expression using the statistical CSI. We next formulate a joint optimization problem to maximize the sum SE of the system. We first introduce a novel successive Quadratic Transform (successive-QT) algorithm to optimize the transmit power coefficients using the concept of block optimization along with quadratic transform and then use the particle swarm optimization technique to design the RIS phase shifts. Note that most of the existing works on RIS-aided cell-free systems are specific instances of the general scenario studied in this work. We numerically show that i) the RIS-assisted link is more advantageous at lower transmit power regions where the direct link between AP and user is weak, ii) NOMA outperforms orthogonal multiple access schemes in terms of SE, and iii) the proposed joint optimization framework significantly improves the sum SE of the system.

The spectral efficiency (SE) and global energy efficiency (GEE) trade-off encountered in the design of millimeter-wave (mmWave)-based massive multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) networks is investigated with a particular focus on user clustering. By exploiting the similarity among user channels a pair of spectral and energy-efficient user clustering algorithms are proposed for dynamically selecting both the number of clusters and the number of users in each cluster. Subsequently, a joint analog precoder/combiner and user clustering technique is developed, followed by a multi-objective optimization (MOO) framework for flexibly balancing the GEE and SE objectives in a mmWave NOMA network subject to specific constraints. The MOO objective is initially transformed to a weighted sum rate maximization problem, followed by a quadratic-transform (QT)-based approach conceived for maximizing the non-convex objective by approximating it as a concave-convex function. Our simulation results demonstrate that the user clustering techniques designed attain a 85% performance gain over random clustering technique and demonstrating the benefits of the algorithm designed for mmWave NOMA networks.

This study focuses on the user clustering performance of millimeter-wave (mmWave) massive multipleinput multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks. Two novel spectral and energy-efficient user clustering algorithms are proposed that exploit the similarity between user channels and condition number (CN) as the correlation index, toward dynamic selection of the number of clusters and number of users in each cluster, thus ensuring that the users in different clusters are spatially uncorrelated. Our simulation results demonstrate that the proposed user clustering techniques attain a $100 \%$ performance gain over a naive random clustering technique.

This study focuses on designing a 3GPP-compliant channel deinterleaver for the 5G new radio (NR) physical uplink control channel (PUCCH) for field programmable gate array (FPGA). The PUCCH transmit chain employs a complex triangular structure for channel interleaving which typically requires nested ‘for’ loops, while the channel deinterleaver in the receive chain has to perform the reverse operation using LLRs, which requires handling multiple bits, thereby increasing complexity. We present a novel algorithm for FPGA implementation of the channel deinterleaver with reduced complexity by replacing the nested ‘for’ loop with a single ‘for’ loop and conditional variable increment. Our design is benchmarked using a 2-tier testing strategy with the Matlab 5G NR toolbox, with test vectors covering all configurations. Our FPGA implementation demonstrates that the designed channel deinterleaver completes its operation within a slot duration of 5G NR with minimal resource utilization.

Orthogonal time frequency space (OTFS) waveform provides resilience against doubly dispersive wireless channel resulting in high mobility application. This paper amalgamates OTFS waveform with full-duplex (FD) distributed massive multi-ple input multiple output (mMIMO) communications to achieve high throughput gain in high mobility applications. In particular, we consider an OTFS-aided FD cell-free mMIMO system, where FD access points (APs) simultaneously serve half-duplex (HD) uplink (UL) and downlink (DL) users. We derive novel closed-form spectral efficiency (SE) expressions for both UL and DL in the existence of channel estimation errors. Our simulations validate the correctness of the derived closed-form SE expressions and highlight the impact of velocity on sum SE for two different vehicular models. The simulation results also demonstrate the SE improvement of the FD cell-free mMIMO-OTFS system over its HD counterpart.

Most of the prior research in multipair two-way full-duplex (FD) massive multiple input multiple output (mMIMO) relaying systems considers a Rician channel with a deterministic line of sight component assuming fixed locations of the transceivers. We consider random phase shifts due to phase noise and user mobility in the LoS component to capture a more realistic scenario. We estimate the phase aware and phase unaware Rician channel via the minimum mean square error and linear minimum mean square error-based estimators, respectively, and use a maximal ratio combiner/maximal ratio transmitter to derive the closed-form spectral efficiency (SE) for an arbitrary number of relay antennas. The numerical outcomes validate the correctness of the derived closed-form SE expression when compared with its ergodic equivalent. We numerically investigate the impact of phase shift and loop interference on the SE.

The integration of orthogonal time frequency and space modulation technique (OTFS) and reconfigurable intel-ligent surfaces (RIS) provides a promising solution for better connectivity in high mobility scenarios. Physical layer security (PLS) by exploiting channel characteristics has recently emerged as a promising security solution. We propose a two-fold PLS scheme for communication via RIS using OTFS waveform. As a first fold of security, we design a channel-based precoder for rotating delay-Doppler (DD) information symbols of a legitimate user. Subsequently, an artificial noise (AN) is added with the precoded DD symbols to enable another layer of security. To project the AN onto the channel subspace of a legitimate user and to mitigate its effect on the legitimate user's receiver, we derive a projection matrix by using the superposition of direct and cascaded channels. The strongest channel maximization based phase optimization scheme is also designed. Our results show that, unlike the legitimate user, the eavesdropper is not able to decode the information at all. The simulation results demonstrate the improved bit-error-rate (BER) of the proposed phase optimization technique over the random phase selection and OTFS system without RIS.

5G new radio (NR) distinguishes itself by offering remarkable features such as significantly higher throughput and lower latency compared to 4G. In 5G, low density parity check (LDPC) encoding is employed as the channel coding scheme for the transmission of data bits. Subsequent to the LDPC encoding stage, bit selection and bit interleaving operations are performed. Rate matching plays an important role in selecting specific encoded bits for transmission, utilizing techniques like shortening and repetition to support hybrid automatic repeat request functionality. To address the issue of latency encountered in processing large transport blocks, we propose parallel algorithms for the rate matching. These algorithms are implemented on field programmable gate arrays, and then the performance of the optimized algorithms is compared with the existing algorithms specified in the 3rd generation partnership project standards.

This work focuses on the FPGA implementation of the 3GPP complaint polar encoder for 5G-new radio (NR). We use Xilinx's polar intellectual property (IP) aka polar IP for the polar encoding functionality. We design a polar configuration IP by generating the bit allocation (BA) table, configuring register values and passing the right control parameters. Next, a parameter handler is developed which is a memory mapped module and provides an advanced extensible interface (AXI)-lite interface to the polar IP. To test the functionality of the polar IP, a Verilog test bench is developed. It provides configuration parameters, payload and BA table in compliance with 3GPP standards to the polar configuration IP. We verify the output of our implementation with the Matlab 5G-NR built-in library on the Xilinx evaluation board. Our results show the efficiency of the implementation in terms of latency and resource utilization on the evaluation board.

We consider a cell-free (CF) massive multi-input multi-output (mMIMO) system, where multi-antenna access points (APs) serve single-antenna unmanned aerial vehicles (UAVs) and ground users (GUEs). We assume, unlike the existing CF mMIMO literature, hardware-impaired UAVs and GUEs, which observe a mixture of spatially-correlated Rician- and Rayleigh-faded channels while communicating with hardware-impaired APs. We derive a closed-form downlink spectral efficiency (SE) expression by using practical models for the channel mixture, and by considering channel estimation errors. We propose a novel block quadratic transformation (block-QT) technique to optimize non-convex network-centric global energy efficiency (GEE) by appropriately modeling circuit, UAV propulsion and fronthaul powers. The novel block-QT approach combines block optimization and quadratic transformation technique to decompose GEE optimization into simpler convex sub-problems. We numerically show that i) it is better to operate a UAV at a larger height when it has severe hardware impairments; and ii) when UAVs operate at a lower height, they do not significantly affect the SE of GUEs.

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 a full-duplex cell-free massive multiple-input-multiple-output system with limited capacity fronthaul links. We derive its downlink/uplink closed-form spectral efficiency (SE) lower bounds with maximum-ratio transmission/maximum-ratio combining and optimal uniform quantization. To reduce carbon footprint, this paper maximizes the non-convex weighted sum energy efficiency (WSEE) via downlink and uplink power control, and successive convex approximation framework. We show that with low fronthaul capacity, the system requires a higher number of fronthaul quantization bits to achieve high SE and WSEE. For high fronthaul capacity, higher number of bits, however, achieves high SE but a reduced WSEE.

We consider a multi-way massive multi-input multi-output (mMIMO) relay via which several energy-constrained MIMO users exchange information by switching between wireless power generation and information transfer. The mMIMO relay and MIMO users experience spatially-correlated channels, which the current multi-way relaying literature ignores. We derive closed-form spectral efficiency (SE) expression for this system that is valid for a practical number of relay antennas. We design a max-min power control algorithm which ensures user fairness by jointly optimizing the charging time and transmit powers of the relay and users.

We consider an unmanned aerial vehicle (UAV) enabled cell-free (CF) massive multi-input multi-output (mMIMO) system, where multi-antenna access points (APs) assist various single-antenna UAVs and ground users (GUEs). Unlike, existing CF mMIMO works, we assume non-ideal transceiver hardware at the UAVs, GUEs and APs, and derive a closed-form downlink spectral efficiency (SE) expression by considering spatially-correlated Rician channels and channel estimation errors. We also propose a novel block quadratic transformation technique to optimize the system global energy-efficiency (GEE) by incorporating practical backhaul power, circuit and UAV propulsion power. We numerically show that severely hardware-impaired UAVs will have a higher SE when they operate at a larger height.

Most of the existing massive multi-input-multi-output (mMIMO) relaying works assume ideal transceiver hardware and spatially uncorrelated channels. A practical mMIMO relay, however, due to limited antenna spacing and the use of cost-effective equipment, usually experiences spatially-correlated channels and hardware impairments, respectively. We consider a hardware-impaired two-way full-duplex (FD) spatially-correlated mMIMO relaying with multiple MIMO FD user-pairs, and derive a closed-form spectral efficiency (SE) expression which is valid for a practical number of finite relay antennas. We use this expression to develop a quadratic transformation approach to optimize the non-convex global energy efficiency (GEE) and weighted sum energy efficiency (WSEE) metrics, which are fractional functions of optimization variables. The proposed approach first converts the fractional problem into its equivalent fraction-free counterpart, and then uses novel transformations to make the problem concave, and solve it using an iterative algorithm. We use this optimization to i) investigate the impact of spatial correlation and hardware impairments on GEE; ii) decide hardware impairment values for a tolerable reduction in GEE; and iii) investigate the impact of weights in WSEE on the users energy efficiency.

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 a massive multi-input multi-output (mMIMO) system where in a base station (BS) serves its users via multiple relays by employing non-orthogonal multiple access (NOMA). We practically model this system by considering channel estimation errors both at the BS and at the users, which consequently perform imperfect successive interference cancellation. We consider these two artifacts, and derive a lower bound on the spectral efficiency (SE) of this multi-relay NOMA system, which is valid for arbitrary number of BS antennas. We then jointly allocate the BS and relay powers to optimize non-convex global energy efficiency (GEE) and the weighted sum energy efficiency (WSEE) metrics, which are fractional functions of sum-of-products/ratios of optimization variables. These two optimizations require extension of an existing fractional programming framework, which we do by proposing two novel transformations. We analytically prove the monotonic convergence of the transformed problems to a stationary point of their respective original counterparts. We numerically show that i) a multi-relay mMIMO NOMA system outperforms its orthogonal counterpart only with accurate channel information; and ii) the proposed GEE and WSEE algorithms significantly improve the energy efficiency by avoiding additional power use, after attaining optimal value.

We optimize the weighted sum energy efficiency (WSEE) of two-way amplify and forward relaying, where multiple full-duplex (FD) user-pairs exchange information via a shared FD massive multiple-input multiple-output (MIMO) relay. Optimization of user-centric WSEE metric, which prioritizes links of users with high energy efficiency (EE) requirements by suitably choosing their weights, is a non-convex problem due to its sum-of-ratio form. We optimize it by first approximating WSEE as a concave-convex fractional function, and then by using the quadratic transform. We then use Karush-Kuhn-Tucker (KKT) conditions to derive a closed-form solution to optimize WSEE. We numerically show that the i) proposed solutions achieve significant WSEE gains; and ii) suitable choice of weights can help prioritize EE requirements of different users.

Design of energy-efficient wireless systems has recently attracted attention to reduce their carbon footprint. This paper optimizes non-convex weighted sum energy efficiency (WSEE) of a multi-pair two-way amplify-and-forward half-duplex massive multiple-input multiple output relay system. We optimize it by developing a two-layer decentralized successive convex approximation optimization framework. The first layer approximates the non-convex WSEE either as a generic convex program (GCP) or as a second order cone program (SOCP). The second layer decentrally solves the approximated problem using alternating direction method of multipliers. We show that the proposed iterative algorithm yields a Karush-Kuhn-Tucker point of the original WSEE problem. We numerically analyze the effect of weights on the energy efficiency (EE) of individual users, and show that the proposed framework enable us to meet the heterogeneous EE requirements. We also analytically and numerically show that the decentralized algorithm has lesser complexity than its centralized counterpart, but yields the same WSEE.

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.

We consider a multi-pair two-way full-duplex (FD) amplify-and-forward relaying, where multiple FD multi-input multi-output (MIMO) users exchange information via a shared FD massive MIMO relay. We derive a closed-form spectral efficiency (SE) lower-bound for maximal-ratio combining/maximal-ratio transmission relay processing considering spatially correlated relay and user antennas, which most of the existing works have ignored. The derived SE lower-bound is applicable for arbitrary number of relay antennas, and simplifies to the following results available in the literature i) the asymptotic SE for number of relay antennas tending to infinity; and ii) the SE lower-bound with independent relay antennas and single-antenna users. We use this SE lower-bound to maximize the non-convex SE, which is of matrix fractional form. We optimize SE by approximating it as concave-convex function, and then by applying matrix quadratic transformation. We numerically validate the SE lower-bound for various system configurations and also characterize their performance for different spatial correlation and interference values. We investigate the gains achieved by the optimal SE over equal power allocation.

We consider two-way amplify-and-forward relaying, where multiple full-duplex user pairs exchange information via a shared full-duplex massive multiple-input multiple-output (MIMO) relay. Most of the previous massive MIMO relaying works maximize the spectral efficiency (SE). By contrast, we maximize the non-convex energy efficiency (EE) metric by approximating it as a pseudo-concave problem, which is then solved using the classic Dinkelbach approach. We also maximize EE of the least energy-efficient user relying on the max-min approach. We also compare SE and EE of the proposed design with existing full-duplex systems and quantify the significant improvement achieved by the proposed algorithm. We also compare EE of the proposed full-duplex system to that of its half-duplex counterparts, and characterize the self-loop and inter-user interference regimes, for which the proposed full-duplex system outperforms the half-duplex ones.

We consider two-way amplify and forward relaying, where multiple full-duplex user pairs exchange information via a shared full-duplex massive multiple-input multiple-output (MIMO) relay. We derive closed-form lower bound for the spectral efficiency with zero-forcing processing at the relay, by using minimum mean squared error channel estimation. The zero-forcing lower bound for the system model considered herein, which is valid for arbitrary number of antennas, is not yet derived in the massive MIMO relaying literature. We numerically demonstrate the accuracy of the derived lower bound and the performance improvement achieved using zero-forcing processing. We also numerically demonstrate the spectral gains achieved by a full-duplex system over a half-duplex one for various antenna regimes.