Non-wide sense stationarity in the spatial dimension gives rise to partial visibility regions (VR) in the channels pertinent to extra-large (XL) antenna arrays (ELAAs). This paper investigates the detrimental impact of partial VRs for multi-cell XL multiple-input multiple-output (MIMO) non-orthogonal multiple-access (NOMA) systems due to VR-unaware precoding. We present an achievable rate analysis by modeling channels with VRs and hence to capture its effects on the downlink (DL) precoders. This rate analysis considers VR-aware precoding at the ELAAs, and thus, it can be used to study the rate losses due to VR-unaware precoding. This rate analysis also captures adverse effects of erroneously estimated uplink and DL channels, correlated fading, imperfect successive interference cancellation (SIC), and intra-cluster/cell pilot contamination. A VR-aware transmit power optimization is also proposed to achieve a system-wide common user rate across all NOMA clusters. We unveil that full visibility assumption for XL-MIMO leads to overestimation of user rates. To circumvent this, it is advocated to capture partial VRs in channel modeling and invoke VR-aware precoders when ELAAs are deployed to serve distributed NOMA clusters. To minimize the adverse effects of weakened channel hardening due to partial VRs and imperfect SIC at the users, it is also advocated to precode DL pilots such that the users adopt estimated channel state information to decode DL NOMA signals.

An intelligent reflecting surface (IRS) aided cell-free massive multiple-input multiple-output (CF-mMIMO) system with underlay spectrum sharing is investigated. The achievable rates for the primary and secondary users are derived in closed-form by considering imperfectly estimated cascaded channel state information (CSI) through linear minimum mean square error estimation, spatially correlated fading, residual interference caused by underlay spectrum sharing based primary/secondary system deployment, and beamforming/decision uncertainties. An IRS phase-shift optimization problem is formulated to minimize the secondary interference inflicted on the primary system based on the underlay spectrum sharing concept. The proposed phase-shift optimization procedure reduces the pilot overhead and computational complexity as its solution depends solely on statistical CSI. Our achievable rate analysis is validated through Monte-Carlo simulations.

The recently acquired mid-band frequency range (FR3) for 6G necessitates adopting extremely large aperture arrays (ELAAs) to leverage higher array gains and spatial multiplexing gains to compensate for larger path-losses compared to sub-6 GHz band and reduction of bandwidth availability compared to millimeter-waves, respectively. However, the nearfield of ELAAs may extend hundreds of meters depending on the aperture size and operating frequency. Hence, the planarwave based far-field channel models must be replaced by spherical-wave based near-field counterparts. To this end, we analyze the near-field performance of ELAA-based integrated sensing and communications (ISAC). This analysis captures the near-field spatial correlation, partial visibility due to spatially non-wide sense stationarities, erroneous channel estimates, an extended target, and clutter sources. A computationally-efficient conjugate precoding-based superimposed ISAC waveform is used at ELAAs. This waveform is further optimized via transmit power allocation to maximize the minimum achievable rate of the weakest communication user, while satisfying a sensing threshold for target detection. The achievable user rates and a target detector are derived. Our results demonstrate the potential of ELAA-based ISAC to improve the trade-off between the communication and sensing performance metrics.

The phase-shift control of intelligent reflective surface (IRS)-aided massive multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) systems requires pilot-intensive instantaneous estimation of cascaded channels. As a remedy, in this paper, statistical composite channel state information (CSI)-based techniques for SWIPT in IRS-aided massive MIMO are explored. Two phase-shift/power optimization strategies are proposed to guarantee system-wide user-fairness in terms of the average harvested energy and achievable rates, while keeping the pilot overhead and computational complexity significantly lower than the current state-of-the-art counterparts. By considering a hybrid time-switching/power-splitting protocol with a non-linear energy harvesting model, the average harvested energy and achievable rates are quantified. This analysis considers spatially correlated fading, imperfectly estimated composite channels, and optimization of IRS phase-shifts and transmit power control based on statistical composite CSI. The design insights and performance gains of the proposed techniques are presented through numerical results. Monte-Carlo simulations are used to validate our analysis.

Spatial non wide-sense stationarities cause partial visibility regions (VRs), and it is a unique propagation characteristic of emerging extra-large aperture arrays (ELAAs). Thus, classification of VRs is a necessity for accurate estimation of channels and efficient design of VR-aware precoders for ELAAs. In this paper, a deep learning framework is proposed to classify VRs in ELAAs. Our objective is to boost the accuracy of classifying VRs based on the uplink pilots received at the ELAAs. Consequently, we focus on guaranteeing user-fairness in the presence of wholly/partial VRs and improving the achievable rates by adopting VR-aware channel estimation and precoding. We propose a hybrid deep learning architecture comprising one dimensional convolutional neural networks and long-short term memory to classify VRs of each user at the ELAA. To achieve a higher accuracy, we generate a diverse dataset through Monte-Carlo simulations that captures numerous combinations of VRs at the ELAA. A transmit power allocation algorithm is also proposed to achieve a common downlink rate for all users irrespective of the different VRs, and its computational complexity is discussed. A set of numerical results is presented to evaluate the performance of our proposed framework. It is efficient and accurate in classifying VRs. Thus, it can be used to enhance the estimation accuracy of ELAA channels with VRs and thereby to design VR-aware precoders to boost spectral/energy efficiency of the next-generation wireless systems.

In downlink (DL) rate-splitting multiple access (RSMA), the users rely on successive interference cancellation (SIC) to remove contribution of the common signal prior to decoding their private messages. Since the users in time division duplexing based massive multiple-input multiple-output (MIMO) set-ups also rely on statistical channel state information (CSI) for signal decoding, perfect SIC may not be practically viable, specifically in the absence of DL pilots. Hence, this paper investigates the deleterious impact of imperfect SIC on the achievable rate for reconfigurable intelligent surface (RIS)-aided massive MIMO systems with RSMA. Our analysis quantifies the sum rate loss due to imperfect SIC in the presence of erroneously estimated cascaded channels, spatially correlated fading, and statistical CSI at the users. A transmit power control algorithm is also proposed to maximize the minimum achievable private rates, while guaranteeing that the minimum common rate at any user is no smaller than the optimized private rate. Our power allocation strategy ensures system-wide user fairness for the DL of RIS-aided massive MIMO RSMA that relies on statistical CSI.

The achievable rate performance of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided massive multiple-input multiple-output (MIMO) systems is investigated. Specifically, the achievable user rates are derived for three operating protocols of the STAR-RIS, namely the energy-splitting (ES), mode-switching (MS), and time-switching (TS) with both unicast and multicast transmissions. This analysis is useful in evaluating the system performance under imperfectly estimated channel state information (CSI), spatially correlated fading, pilot contamination, and statistical CSI based phase-shift optimization, transmit power control, and user signal decoding. For the high signal-to-noise ratio regime, the asymptotic achievable rates are also derived, and they serve as benchmarks or upper bounds for the rate performance comparisons for systems operating under the above transmission impediments. The composite uplink channels are estimated through linear minimum mean square error estimation techniques, and the phase-shift matrices at the STAR-RIS are optimized to maximize the effective average channel gains to minimize the channel estimation overhead. The base-station optimizes the transmit power based on the max-min criterion to attain a system-wide common user rate by negating the near-far effects of the downlink composite channels. Our numerical and simulation results validate our theoretical analysis and convergence of phase-shift and transmit power optimization algorithms. Our analytical and simulation results are useful in investigating the performance gains/comparisons among the ES, MS, and TS protocols for unicast and multicast transmissions to enable 360° smart coverage extensions with passive STAR-RIS aided massive MIMO.

The active pilot contamination attacks in intelligent reflecting surface (IRS) aided massive multiple-input multiple-output systems are investigated. By proposing a statistical channel state information based IRS phase-shift optimization technique, an achievable secrecy rate is derived in the presence of practical impediments, including erroneously estimated composite channels via linear minimum mean square error estimation criterion, residual interference due to active pilot contamination, artificial noise (AN) generation, and spatially correlated fading at the base-station antennas and IRS elements. A transmit power allocation technique is also proposed. Two active pilot attack detectors are designed based on the Neyman-Pearson and generalized likelihood ratio test criteria. The performance of these detectors is investigated by deriving the probability of detection, probability of false alarm, and receiver operating characteristics. Our secrecy rate analysis reveals that the rate leaked into the eavesdroppers by active pilot contamination attacks can be considerably high. The proposed power allocation algorithm jointly assigns transmit powers for the legitimate signals and AN sequences for maximizing the minimum secrecy rate of the weakest legitimate user to ensure user-fairness. The proposed detectors of active pilot attacks may be useful in designing remedial techniques to mitigate detrimental effects of active eavesdropping.

The achievable rates and computationally efficient statistical channel state information (CSI) based phase-shift and transmit power optimization techniques are investigated for multi-cell reconfigurable intelligent surface (RIS)-aided multi-user massive multiple-input multiple-output (MIMO). The uplink effective composite channels are estimated via linear minimum mean square error techniques. The channel covariance matrices are adopted to optimize the RIS phase-shifts to maximize the average sum power gains of the composite channels pertaining to all users, while minimizing the inter-cell interference. The proposed transmit power control algorithm maximizes the minimum user rate across all cells to achieve a common rate, while ensuring user-fairness by negating near-far effects. The performance of these techniques is evaluated by deriving the achievable user rates in closed-form by presenting two lemmas and two corollaries. These new results can be useful in accurate performance analysis of RIS-aided massive MIMO without invoking typical approximations based on the central limit theorem and moment matching with Gamma distribution. The achievable user rate analysis can also be used to evaluate the impact of spatially correlated fading, erroneously estimated CSI, intra-cell co-channel interference, pilot contamination, and statistical CSI-based user signal decoding. The pilot overhead and computational complexity of the proposed techniques are quantified. Thereby, we reveal that the proposed phase-shift optimization technique is both computationally efficient and scalable with large numbers of reflective elements and BS antennas. Our achievable rate analysis and convergence of the optimization algorithms are validated through Monte-Carlo simulations and numerical results.

We investigate the achievable rate of extra-large reconfigurable intelligent surface (XL RIS) aided downlink massive multiple-input multiple-output (MIMO) in the presence of wholly/partial visibility regions, spatial correlation, and imperfectly estimated channel state information (CSI). To reduce high pilot overhead associated with XL RIS-aided massive MIMO, a statistical CSI-based phase-shift optimization technique is adopted to maximize the minimum sum of eigenvalues of user covariance matrices. To ensure user-fairness in the presence of wholly/partial visibility regions, a transmit power allocation technique is designed based on a max-min optimization criterion. We present numerical/simulation results to validate our analysis/optimization solutions, to investigate the effects of wholly/partial visibility regions, and to reveal the performance gain of the XL RIS-aided massive MIMO systems.

We investigate statistical channel state information (CSI) based phase-shift and transmit power optimization techniques for the reconfigurable intelligent surface (RIS)-aided multi-user massive multiple-input multiple-output (MIMO) systems. Towards this end, the uplink composite channel at the massive MIMO base-station is estimated by using uplink pilots sent by the users via linear minimum mean square error estimation for an arbitrary phase-shift matrix at the RIS. Then, the RIS phase-shift matrix is iteratively optimized to maximize the sum of eigenvalues of the correlation matrix of the weakest user. Thereby, a maximal ratio transmission based precoder is designed by using the estimated composite channel together with the statistical CSI based optimal phase-shift matrix for the downlink payload transmission. Via the worst-case Gaussian technique, the downlink achievable user rates are derived in closed-form for correlated Rayleigh fading in the presence of imperfect CSI. A max-min based transmit power optimization algorithm is proposed to provide a common system-wide achievable user rate and thereby ensuring user-fairness while mitigating near-far effects. Monte-Carlo simulations are presented to validate our rate analysis, to exhibit the convergence of our proposed optimization algorithms, and to reveal system-design insights.

On the contrary to general consensus, we reveal that the best intelligent reflecting surface (IRS) selection outperforms the distributed IRSs in the presence of uniformly distributed phase-shift errors over [-π, π), which is the worst case for im-perfectly estimated channel phases. Nevertheless, the distributed IRS set-up regains its dominance when the channel phases are perfectly estimated and even for the case of discrete phase-shift adjustments with quantization errors. In this context, the outage performance of the best IRS selection is investigated and compared against the distributed IRS scheme with phase-shift errors. The best IRS selection criterion is designed to maximize the signal-to-noise ratio (SNR) by jointly optimizing the phase-shifts of passive reflecting elements at the best IRS. The end-to-end SNR for the Nakagami-m fading is statistically characterized by deriving a tight approximation to the cumulative distribution function in the presence of phase-shift errors at the IRSs. Our Monte-Carlo simulation results validate our analysis, and our numerical results compare the performance of the best IRS selection and distributed IRS set-ups.

A performance analysis of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR- RIS) aided wireless communication system is presented. To this end, we present tight bounds/approximations for the outage probability, average achievable rate, and average symbol error rate (SER) in closed-form for the energy-splitting (ES) and mode-switching (MS) protocols. A high signal-to-noise ratio (SNR) analysis is also presented to obtain useful design insights. Thereby, we show that for a particular user, the asymptotic performance metrics depend on the transmit power allocation coefficients for the reflecting and transmitting users for a STAR- RIS aided communication setup. Our Monte-Carlo simulations validate our closed-form analysis, and our numerical results are used to obtain useful insights for STAR-RIS aided wireless communications.

We propose a graph-theoretic analytical framework to solve the sum rate maximization problem of a non-orthogonal-multiple-access (NOMA)-aided distributed millimeter wave massive multiple-input multiple-output (MIMO) system. The optimal solution for this system-wide sum rate maximization problem is neither mathematically tractable nor computationally-efficient when a traditional communication-theoretic analytical approach is solely invoked. Thus, the original problem is decoupled into two sub-problems, namely, a user access point (AP) association/clustering and a pilot resource allocation. In the first subproblem, APs optimally select a set of users having the highest average channel power gains, while the second sub-problem optimally assigns a set of limited orthogonal pilots among concurrently served users such that the pilot contamination is minimized. We propose a graph-theoretic analytical framework to find practically-viable and computationally-efficient solutions to both these sub-problems by virtue of modeling them via bipartite graph matching and vertex coloring problems. Thereby, we propose an algorithm to compute the minimum number of orthogonal pilots required for a given user-AP association/clustering. By exploiting the minimum pilot length and leveraging the benefits of our graph-theoretic approach, we propose a pragmatic solution of the coexistence of NOMA and orthogonal multiple-access schemes to further boost the achievable rate performance of our proposed system set-up.

The performance of an intelligent reflecting surface (IRS)-assisted time-switching simultaneous wireless information and power transfer (SWIPT) system is investigated from a maxmin user-fairness perspective. A series of optimization problems is formulated to maximize the minimum harvested energy and the achievable user rates via jointly optimizing the transmit powers at the base-station (BS) and phase-shifts at the IRS. The underlying optimization problems are non-convex, and thus, the efficient alternating optimization algorithms are developed to obtain sub-optimal solutions. A combination of geometric programming and convex optimization techniques has been employed in an iterative manner to solve the transmit power allocation and IRS phase-shift optimization problems, respectively. Max-min based common/system-wide harvested energy and achievable user rate are characterized when the BS adopts linear/conjugate precoding. Thereby, a max-min fairness-based energy-rate trade-off is quantified. Our numerical results validate the proposed optimization solutions and reveal the underlying performance gains of the optimized system.

A statistical characterization of the fundamental performance bounds of an intelligent reflective surface (IRS) intended for aiding wireless communications is presented. To this end, the outage probability, average symbol error probability and achievable rate bounds are derived in closed-form. By virtue of an asymptotic analysis in high signal-to-noise ratio (SNR) regime, the achievable diversity order is derived. Thereby, we show that a diversity gain in the order of the number of passive reflective elements embedded within the IRS can be achieved with only controllable phase adjustments. Thus, IRS has a great potential of boosting the wireless performance by intelligently controlling the propagation channels without employing additional active radio frequency chains.