This paper presents the first bit error rate (BER) analysis of a pinching-antenna (PA)-based non-orthogonal multiple access (NOMA) communication system. The PA is assumed to be able to be placed anywhere along the waveguide and serves two NOMA user equipment (UEs) in both uplink (UL) and downlink (DL) scenarios. Exact closed-form expressions for the average BER of each user are derived under practical imperfect successive interference cancellation (SIC). These expressions are then used to optimize the PA location for minimizing the overall average BER of both UEs. In the UL case, the interference between the users’ channels introduces phase-dependent fluctuations in the BER cost function, making it highly non-convex with many local extrema. To address this challenge, a smoothing technique is applied to extract the lower envelope of the BER function, effectively suppressing ripples and enabling a reliable identification of the global minimum. In the DL case, a joint optimization of the PA location and NOMA power allocation coefficients is proposed to minimize the average BER. Simulation results verify the accuracy of the analytical derivations and the effectiveness of the proposed optimization methods. Notably, the UL results demonstrate that an optimally positioned PA can create the required received power difference between two equally powered UEs for reliable power-domain NOMA decoding under imperfect SIC.

This paper proposes a novel Bayesian integrated tracking and communication (ITAC) framework. By consolidating the time of arrival (ToA) and angle of departure (AoD) measurement models, a robust Bayesian filtering framework is introduced for single-target tracking in dynamic and cluttered wireless environments. By incorporating random finite set(RFS) theory, the method jointly estimates the target’s existence probability and its kinematic state within a unified Bayesian recursion, effectively overcoming the limitations of conventional filters, which assume that the target can always be detected. Furthermore, the paper derives the signal-to-interference-plus noise ratio (SINR) for both communication and sensing links. The proposed RFS-based Bayesian filter is implemented using a sequential Monte Carlo (SMC) approach. Numerical simulations demonstrate the framework’s superior performance in maintaining tracking consistency and accuracy even under challenging propagation conditions. Moreover, the results reveal how different network parameters affect the overall communication and tracking performance.

Continuous aperture arrays (CAPAs) provide a theoretical upper bound on the performance of densely packed antenna arrays, but their analysis is limited by the lack of closed-form signal-to-noise ratio (SNR) distributions under realistic fading conditions. This paper derives accurate analytical expressions for the matched-filter SNR distribution of one-dimensional CAPAs in correlated Rayleigh environments under both the sinc and Jakes correlation models using the Karhunen–Loève expansion. By applying a truncated hypoexponential model, we obtain accurate approximations for the probability density function and cumulative distribution function of the SNR that closely match simulations, including the outage probability region where precise characterization is critical. Compared to a standard gamma approximation, our approach provides significantly improved accuracy in this regime. Additionally, the CAPA system considered is shown to outperform discrete antenna arrays. The derived expressions enable tractable and accurate evaluation of CAPAs under practical channel models.

This paper introduces delay-alignment modulation (DAM) for secure integrated sensing and communication (ISAC). Due to the broadcast nature of multi-user downlinks, communications are vulnerable to eavesdropping. DAM applies controlled per-path symbol delays at the transmitter to coherently align the multipath components at the intended user, enhancing the received signal power, while simultaneously creating delay misalignment at the eavesdropper (Eve). To mitigate sensing degradation caused by multipath propagation, we propose a two-stage protocol that first estimates the angle and then the delay of the line-of-sight (LoS) path after suppressing multipath interference. We derive the secrecy spectral efficiency (SSE) and the Cramer–Rao bound (CRB) of the target delay. Finally, we develop a path-based zero-forcing (ZF) precoding framework and formulate a max–min SSE design under CRB and power constraints. Simulation results show DAM significantly outperforms the strongest-path (SP) benchmark in terms of SSE, while meeting sensing requirements, since intentional delay alignment at legitimate users degrades Eve’s reception.

This paper proposes a distributed continuous aperture array (D-CAPA) to support simultaneous wireless information and power transfer (SWIPT) to multiple information users (IUs) and energy users (EUs). Each metasurface supports continuous surface currents that radiate electromagnetic (EM) waves for information and energy transmission to the users. These waves propagate through continuous EM channels characterized by the dyadic Green’s function. We formulate a system power consumption (PC) minimization problem subject to spectral efficiency and energy harvesting quality-of-service (QoS) requirements, where the QoS requirements are derived under the equal power allocation (EPA) scheme. An efficient two-layer optimization algorithm is developed to solve this problem by optimizing the power allocation subject to the QoS violation penalties using augmented Lagrangian transformation. Our numerical results show that well-optimized current distributions over each metasurface in the proposed D-CAPA achieve up to 65% and 61% reductions in overall system PC compared to the EPA and co-located CAPA (C-CAPA) cases, while maintaining the same total aperture size and transmission power.

We propose a novel pinching-antenna-assisted integrated sensing and communication (PAA-ISAC) framework, where the dielectric waveguide enables the downlink transmission and the reflected signal is collected at the base station (BS) for radio sensing. Due to the asymmetrical propagation between the waveguide → sensing target (ST) and the ST → BS, the reflection law is exploited to model the radio sensing propagation. The sensing degree of freedom (SDoF) and Cramér–Rao Bound (CRB) of the estimated angle are first analyzed, where the qualitative relationship between the CRB and the antenna design is revealed. Capitalizing on this finding, a weighted CRB and transmit power minimization problem is formulated subject to dual-functional requirements. A low-complexity alternating optimization algorithm is proposed to jointly optimize the location of the activated pinching antenna (PA) and the transmit power towards the waveguide by invoking the Lagrange duality theory. Our simulation results showcase that: 1) the derived performance approximation is close to the exact CRB; 2) the SDoF is independent of the waveguide deployment; 3) the proposed algorithm outperforms the fixed antenna design in the context of radio sensing performance and the transmit power consumption.

This paper proposes a novel optimization framework for enhancing the security resilience of cell-free massive multiple-input multiple-output (CF-mMIMO) networks with multi-antenna access points (APs) and protective partial zero-forcing (PPZF) under active eavesdropping. Based on the main principles of absorption, adaptation, and recovery, we formulate a security aware resilience metric to quantify the system performance during and after a security outage. A multi-user service priority-aware power allocation problem is formulated to minimize the mean squared error (MSE) between real-time and desired security efficiency, thereby enabling a trade-off between the target user’s secrecy performance and multi-user quality of service (QoS). To solve this non-convex problem, a security-aware iterative algorithm based on the successive convex approximation (SCA) is employed. The proposed algorithm determines the optimal power allocation strategy by balancing solution quality against recovery time. At each iteration, it evaluates the overall resilience score and selects the strategy that achieves the highest value. Simulation results confirm that the proposed framework significantly improves the resilience of CF-mMIMO networks, allowing flexible adaptation between rapid recovery and high-quality recovery, depending on system requirements.

Remote and resource-constrained Internet-of Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.

This paper investigates a multi-user pinching-antenna (PA) system, where a single PA is activated on each waveguide. With the maximum ratio transmission (MRT) beamforming, the system spectral efficiency (SE) is studied, where the inter-user interference term complicates the analysis of the SE. To overcome this obstacle, the stationary phase point method (SPPM) is applied to obtain an analytically tractable form of the SE. The analysis reveals that the average inter-user interference can be negligible with a large waveguide spacing even using the MRT. This insight makes the simple MRT appealing for PA-based multi-user communications. Finally, the theoretical analysis is verified through simulations. Our numerical results confirm that 1) with the aid of SPPM, the approximation of the system SE is accurate; 2) and while increasing the waveguide spacing helps reduce the average inter-user interference, it might degrade the SE due to the increased signal propagation path loss.

Specific emitter identification (SEI) exploits device-dependent RF fingerprints to distinguish individual transmitters and is important for securing large-scale Internet-of-Things (IoT) deployments. While deep SEI can achieve near-perfect accuracy under same-day evaluation, real deployments rarely satisfy this assumption. At scale, per-day labeling is infeasible; models must therefore generalize from a labeled source day to an unlabeled target day, where day-to-day propagation drift induces distribution shifts and can substantially degrade performance under direct transfer (without adaptation). To address this challenge, we propose a unified unsupervised domain adaptation (UDA) framework for cross-day SEI that requires neither hardware calibration nor handcrafted features. The proposed objective integrates adversarial domain alignment, confidence-aware pseudo-labeling to exploit high-confidence target samples safely, and a cross-domain contrastive regularizer to preserve class-discriminative geometry. We further provide an analysis offering insight into how each component contributes to target-domain generalization. Experiments on two public RF benchmarks from different wireless technologies demonstrate robust cross-day performance across diverse transfers. On WiSig–ManySig, our method achieves 99.78% mean cross-day accuracy over six source-to-target day transfers, ranking best in five cases and remaining within 0.04% of the best in the remaining case. On a LoRa benchmark, it achieves 90.64% mean cross-day accuracy over ten day-transfer pairs, validating the framework beyond Wi-Fi and under larger transfer diversity.

5G-Advanced (5G-A) has enabled the vibrant development of low altitude integrated sensing and communication (LA-ISAC) networks. As a core component of these networks, unmanned aerial vehicles (UAVs) have witnessed rapid proliferation in recent years. However, due to the lag in traditional industry regulatory norms, unauthorized flight incidents occur frequently, posing a severe security threat to LA-ISAC networks. To surveil the non-cooperative UAVs, in this paper, we propose a multi-domain supervised contrastive learning (MD-SupContrast) framework for UAV radio frequency (RF) open-set recognition. Specifically, first, the texture features and the time-frequency position features from the ResNet and the TransformerEncoder (TE) are fused, and then the supervised contrastive learning is applied to optimize the feature representation of the closed-set samples. Next, to surveil the invasive UAVs that appear in real life, we propose an improved generative OpenMax (IG-OpenMax) algorithm and construct an open-set recognition model, namely Open-RFNet. According to the unknown samples, we first freeze the feature extraction layers and then only retrain the classification layer, which achieves excellent recognition performance both in closed-set and open-set recognitions. We analyze the computational complexity of the proposed model. Experiments are conducted with a large-scale UAV open dataset. The results show that the proposed Open-RFNet outperforms the existing benchmark methods in terms of recognition accuracy between the known and the unknown UAVs, as it achieves 95.12% in closed-set and 96.08% in open-set under 25 UAV types, respectively.

The space-air-ground integrated network (SAGIN) paradigm has emerged as a pivotal enabler for the evolution of next-generation wireless systems. This article proposes a novel framework for covert communication in SAGINs, wherein a high-altitude platform (HAP), equipped with multiple antennas, serves terrestrial communication users (CUs) under the surveillance of multiple non-colluding wardens, with satellite assistance for warden location updates via space-air links. To safeguard the communication from detection by the wardens, the HAP employs artificial noise (AN) and robust beamforming techniques, addressing the challenges posed by imperfect channel state information (CSI) of the wardens. Subsequently, we formulate a non-convex optimization problem aimed at maximizing the number of served users, subject to stringent covertness constraints, satellite-HAP link outage probabilities, and maximum available power budgets. By employing ℓ0-norm relaxation, we convert the original problem into a mixed-integer optimization framework and develop a computationally efficient alternating optimization approach that combines bisection search, successive convex approximation (SCA), and semidefinite relaxation (SDR) techniques to tackle satellite power allocation, user scheduling, and beamforming design. Numerical simulations demonstrate that the proposed scheme significantly improves the user coverage while maintaining covertness, revealing a trade-off between covert communication and CU coverage capability in resource-constrained aerial-terrestrial environments, even under imperfect CSI conditions.

Non-orthogonal multiple access (NOMA) is widely recognized for enhancing the energy and spectral efficiency through effective radio resource sharing. However, uplink NOMA systems face greater challenges than their downlink counterparts, as their bit error rate (BER) performance is hindered by an inherent error floor due to error propagation caused by imperfect successive interference cancellation (SIC). This paper investigates the BER performance improvements enabled by reconfigurable intelligent surfaces (RISs) in multi-user uplink NOMA transmission. Specifically, we propose a novel RIS-assisted uplink NOMA design, where the RIS phase shifts are optimized to enhance the received signal amplitudes while mitigating the phase rotations induced by the channel. To achieve this, we first develop an accurate channel model for the effective user channels, which facilitates our BER analysis. We then introduce a channel alignment scheme for a two-user scenario, enabling efficient SIC-based detection and deriving closed-form BER expressions. We further extend the analysis to a generalized setup with an arbitrary number of users and modulation orders for quadrature amplitude modulation signaling. The analysis is also extended to consider imperfect channel state information (CSI) knowledge and the multi-antenna base station (BS) cases. Using the derived BER expressions, we develop an optimized uplink NOMA power allocation (PA) scheme to minimize the average BER while satisfying the user transmit power constraints. It will be shown that the proposed NOMA detection scheme, in conjunction with the optimized PA strategy, eliminate SIC error floors at the base station. The theoretical BER expressions are validated using simulations, which confirms the effectiveness of the proposed design in eliminating BER floors.

This paper considers a practical cell-free massive multiple-input multiple-output (CF-mMIMO) architecture within an open radio access network, where edge distributed units (EDUs) and user-centric distributed units (UCDUs) collaboratively handle physical-layer functions (e.g., channel estimation, precoding), while the open radio units (ORUs) are responsible for radio-frequency transmission and reception with the user equipment (UE). Based on large-dimensional random matrix theory, we derive a deterministic equivalent (DE) expression for the ergodic sum SE under imperfect statistical channel state information (S-CSI). Thanks to this DE-assisted result, two optimization problems: 1) sum power minimization, and 2) ergodic sum spectral efficiency (SE) maximization, are addressed through regularized parameter tuning in local partial regularized zero-forcing (LP-RZF), and power control with large-scale fading (LSF)-based EDU-ORU deployment and ORU-UE association. Numerical results validate the tightness of the DE-based ergodic sum SE expression and demonstrate the effectiveness of the LP-RZF scheme compared to the benchmark schemes. Meanwhile, the reduced computational complexity is achieved with acceptable performance loss.

Terahertz (THz) frequency band has been widely studied and is recognized as a promising candidate for centimeter-level localization. However, the limited coverage of THz networks may result in localization failures, while a heterogeneous deployment of millimeter-wave (mmWave) and THz radio units (RUs) offers a viable solution to mitigate this issue. This paper presents a theoretical framework for evaluating the performance limits of localization systems in mmWave and THz heterogeneous networks. In this architecture, the mmWave RUs serve as macro base stations (BSs), while the THz RUs function as micro BSs distributed around each mmWave RU. By leveraging the standard tools of stochastic geometry to model the spatial distributions of the RUs and ambient obstacles, the localizability of a target is computed to evaluate the probability of achieving sufficient signal-to-interference-plus-noise ratio for localization in both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. Furthermore, the Cram é r-Rao lower bounds in both LoS and NLoS scenarios are analytically derived to characterize the overall positioning performance. Numerical results demonstrate that the hybrid deployment strategy significantly improves both the network coverage and localization accuracy compared to mmWave-only and THz-only networks.

Since the beam squint and near-field effects both inherently exist in upper-6 GHz (U6G) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, wideband near-field channel estimation faces severe challenges, such as higher computational complexity, and higher pilot overhead particularly at hybrid architectures with fewer radio frequency (RF) chains. To precisely reduce the complexity and number of pilots, theparametric symmetry of wideband near-field channelsis explored, such that the channel parameters, including angle, distance, and range, can be decoupled based on the delay variations observed by different antennas. Based on this, adistributed parametric symmetry-based (DPS) algorithm, applicable to U6G XL-MIMO, is proposed. The delays observed by different subarrays are estimated and extrapolated across the local processing units (LPUs) firstly, and then, the channel parameters are decoupled and estimated at the central processing unit (CPU), by only linearly combining the delays from different LPUs. The path gains are calculated at different LPUs, respectively, to reconstruct the channel with low complexity. Since the proposed algorithm does not rely on scanning the polar-domain dictionary, onlya single pilotis required even with hybrid architectures. Furthermore, the computational complexity, multiple-path resolution, Cramér–Rao lower bound (CRLB) and lower bound (LB) of the estimates in hybrid architectures and the DPS algorithm, respectively, are analyzed, to evaluate the realizable potential of the proposed algorithm. The simulation results prove that the proposed algorithm has a higher estimation accuracy, while requiring less complexity and pilots.

This paper examines the power consumption (PC) efficiency of a mixed near- and far-field (MF) simultaneous wireless information and power transfer (SWIPT) system underpinned by a hybrid beamforming (HB)-based modular extra-large multiple-input-multiple output (XL-MIMO) array. Multiple information decoding (ID) and energy harvesting (EH) users are served by multiple constituent subarrays in both the near-field (NF) and far-field (FF) region of the transmit array. A novel decision method is proposed for accurate classification of different field users using Frobenius norm-based frequency correlation of the least square (LS) channel estimates. The NF spatial non-stationarities (SnS) effects entail distinct electromagnetic (EM) visibility regions (VRs), which can be customized to employ strategic activation of the constituent XL-MIMO subarrays. We formulate a two-tier joint optimization problem to minimize the overall PC, considering the power allocation (PA) for both ID and EH users in addition to the subarray activation (SA). This challenging mixed-integer problem is transformed into computationally tractable formulations, accompanied by the development of well-optimized algorithms. Our simulation results demonstrate an overall PC reduction for our proposed PA-SA-HB scheme by up to 93% against the equal PA with full array (FA) and up to 18% with respect to the PA-FA-HB case.

As a novel antenna array architecture, continuous aperture arrays (CAPAs) have garnered wide attention in recent years. While existing CAPA-based research has predominantly addressed unicast scenarios, the critical area of multicast beamforming remains unexplored. In this paper, a CAPA-based multi-group multicast communication system is investigated. An integral-based CAPA multi-group multicast beamforming design is formulated for the maximization of the system energy efficiency (EE), subject to a minimum multicast SE constraint of each user group and a total transmit power constraint. To address this non-convex fractional programming problem, we employ Dinkelbach’s method, such that the non-convex group-wise multicast spectral efficiency (SE) constraint is first equivalently transformed into a tractable form using auxiliary variables. Then, an efficient block coordinate descent (BCD)-based algorithm is developed to solve the reformulated problem. The CAPA beamforming design subproblem can be optimally solved via the Lagrangian dual method and the calculus of variations (CoV) theory. It reveals that the optimized CAPA beamformer should be a combination of all the groups’ user channels. To further reduce the computational complexity, a low-complexity zero-forcing (ZF)-based approach is proposed. The closed-form ZF CAPA beamformer is derived using each group’s most representative user channel to mitigate the inter-group interference while ensuring the intra-group multicast performance. Then, the beamforming design subproblem in the BCD-based algorithm becomes a convex power allocation subproblem, which can be efficiently solved. Numerical results demonstrate that 1) the CAPA can significantly improve the EE compared to conventional spatially discrete arrays (SPDAs); 2) due to the enhanced spatial resolutions, increasing the aperture size of CAPA is not always beneficial for EE enhancement in multicast scenarios; and 3) wider user distributions of each group cause a significant EE degradation of CAPA compared to SPDA.

This paper investigates a pinching-antenna (PA)-enabled cognitive radio network, where both the primary transmitter (PT) and secondary transmitter (ST) are equipped with a single waveguide and multiple PAs to facilitate simultaneous spectrum sharing. Under a general Ricean fading channel model, a closed-form analytical expression for the average spectral efficiency (SE) achieved by PAs is first derived. Based on this, a sum- SE maximization problem is formulated to jointly optimize the primary and secondary pinching beamforming, subject to system constraints on the transmission power budgets, minimum antenna separation requirements, and feasible PA deployment regions. To address this non-convex problem, a two-stage optimization algorithm is developed, in which stage 1 designs the PT/ST pinching beamforming and stage 2 updates the ST transmit power. For the PT and ST pinching beamforming optimization, the coarse positions of PA are first determined at the waveguide-level. Then, wavelength-level refinements achieve constructive signal combination at the intended user and destructive superposition at the unintended user. For the ST power control, a closed-form solution is derived. Simulation results demonstrate that i) PAs can achieve significant SE improvements over conventional fixed-position antennas; ii) the proposed pinching beamforming design achieves effective interference suppression and superior performance for both even and odd numbers of PAs; and iii) the developed two-stage optimization algorithm enables nearly orthogonal transmission between the primary and secondary networks.

Driven by diverse applications in the emerging low-altitude economy, modern aerial networks must inherently cater for highly heterogeneous environments, characterized by communication services under mixed service delay constraints and diverse user equipment (UE) mobility. However, such heterogeneity leads to resource allocation conflicts and imbalances, which undermine communication reliability and may result in network unavailability. To address this, we investigate resource management in uplink low-altitude heterogeneous networks. Specifically, we propose a flying access point (FAP)-coordinated multi-point packet delivery mechanism with a unified resource allocation (URA) scheme to efficiently manage spatial, frequency, and temporal resources. This includes subchannel allocation, time slot partitioning, and pilot length design. Then, we derive a lower bound (LB) on network availability (NA) and reveal that extended heterogeneity significantly degrades the LB due to: (a) resource reduction under URA and (b) the independence in ensuring services under heterogeneity. To mitigate this degradation, we derive a closed-form condition on the required number of FAPs by relaxing the LB, thereby ensuring sufficient spatial resources to achieve the target NA. Meanwhile, we derive closed-form expressions for jointly approximating the optimal number of UEs sharing time-frequency resources and the pilot length. This optimization improves resource efficiency for NA by balancing the post-processing signal-to-noise ratio and its associated thresholds to satisfy reliability requirements under heterogeneous conditions. Numerical results validate the analysis and demonstrate that the proposed resource management strategy achieves the target NA under increased heterogeneity, thereby outperforming existing approaches.

We consider a reconfigurable intelligent surface (RIS)-assisted extremely large-scale multiple-input multiple-output (XL-MIMO) downlink system, where an XL-MIMO array serves two groups of single-antennas users, namely near-field users (NFUEs) and far-field users (FFUEs). FFUEs are subject to blockage, and their communication is facilitated through the RIS. We consider three precoding schemes at the XL-MIMO array, namely central zero-forcing (CZF), local zero-forcing (LZF) and maximum ratio transmission (MRT). Closed-form expressions for the spectral efficiency (SE) of all users are derived for MRT precoding, while statistical-form expressions are obtained for CZF and LZF processing. A heuristic visibility region (VR) selection algorithm is also introduced to help reduce the computational complexity of the precoding scheme. Furthermore, we devise a two-stage phase shifts design and power control algorithm to maximize the sum of weighted minimum SE of two groups of users with CZF, LZF and MRT precoding schemes. The simulation results indicate that, when equal priority is given to NFUEs and FFUEs, the proposed design improves the sum of the weighted minimum SE by 31.9%, 37.8%, and 119.2% with CZF, LZF, and MRT, respectively, compared to the case with equal power allocation and random phase shifts design. CZF achieves the best performance, while LZF offers comparable results with lower complexity. When prioritizing NFUEs or FFUEs, LZF achieves strong performance for the prioritized group, whereas CZF ensures balanced performance between NFUEs and FFUEs.

Cell-free massive multiple-input multiple-output is a potential candidate for future networks with pervasive connectivity by utilizing coherent joint transmission and distributed antenna arrays. This paper studies the exploitation of full-duplex communication for a distributed antenna array. Specifically, we derive a closed-form expression for the uplink and downlink ergodic spectral efficiency (SE) for a network where the APs can flexibly operate in either the full-duplex or half-duplex mode with linear processing and Rayleigh fading channels. A long-term total SE maximization problem is formulated subject to a network operation model and individual SE requirements with limited power budget. Due to the intrinsic nonconvexity and infeasible circumstances where some UEs might not be able to achieve the rate requirements, we adapt differential evolution to design a low computational complexity algorithm that can attain good power allocation and network operation mode in polynomial time. Numerical results demonstrate the effectiveness of our system design and proposed algorithm over state-of-the-art benchmarks with satisfactory service to the majority of UEs, although several ones may be unscheduled under harsh conditions.

We consider a cell-free massive multiple-input and multiple-output (CF-mMIMO) system, where we derive a deterministic equivalent (DE)-form of the ergodic sum spectral efficiency (SE) based on local partial regularized zero-forcing (LP-RZF) precoding with statistical channel state information (S-CSI) by leveraging large-dimensional random matrix theory. Thanks to this derivation, the previously challenging issue of precoding design based on S-CSI is now resolved, particularly in scenarios where CSI is limited to local information at each access point (AP). Moreover, as the central processing unit (CPU) now only needs to transmit an optimized regularization parameter to the APs, the computational overhead can be reduced, which naturally enhances the system scalability. Driven by these advantages, we then introduce a joint user association, power allocation, and precoding design (i.e., regularization parameter optimization) scheme aimed at maximizing the ergodic sum SE and minimizing the sum power consumption. This is achieved through two optimization problems: one for the ergodic sum SE maximization using weighted minimum mean square error (WMMSE)-based processing and another for the sum power consumption minimization employing a block coordinate descent (BCD)-based algorithm. Numerical results demonstrate the superior performance of the proposed PRO-LPRZF scheme.

wireless communication environment may not experience direct line-of-sight propagation whereas the number of receivers (Rxs) is often limited. We propose the utilization of only non-line-of-sight (NLoS) received signal strength (RSS) measurements observed at a single Rx to locate a target, via a positioning algorithm accounting for data association ambiguity that may occur in a real-world scenario. Considering the stochastic nature of a network geometry, tractable expressions are derived for the probability of acquiring at leastLNLoS RSS measurements during localization. In light of the computational complexity of our solution, we investigate the minimum number of RSS samples required to meet the specified localization accuracy, thereby guiding system design. Furthermore, the probability distribution of the trace of the Cramér-Rao lower bound is obtained analytically, which offers a comprehensive understanding of the fundamental limits of the single-Rx localization scheme without resorting to intensive simulations.

Received signal strength (RSS) has been extensively studied for localization purposes, and the distributed nature of cell-free massive multiple-input multiple-output (CF-mMIMO) systems offers a new synergistic avenue for achieving high-precision localization. In this work, Open RAN and software-defined radio are used to realize the central and distributed units of a CF-mMIMO system to acquire the RSS measurements. By analyzing the experimental data, it is revealed that the RSS measured from the first-order reflection path can yield a sufficiently high signal-to-noise ratio for localization, enabling localization even without line-of-sight (LoS) paths. Inspired by this finding, a hybrid localization scheme, that can attain the best accuracy benchmarked by Cramér-Rao lower bound, is proposed to estimate the target position using both LoS and first-order non-line-of-sight RSS measurements. Furthermore, a theoretical framework is established to assess the fundamental limits of RSS-based localization in CF-mMIMO systems, offering a principled guideline for system designers to deploy and design localization systems in real-world scenarios.

This study explores a next-generation multiple access (NGMA) framework for cell-free massive MIMO (CF-mMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs), aiming to improve simultaneous wireless information and power transfer (SWIPT) performance. A fundamental challenge lies in optimally selecting the operating modes of access points (APs) to jointly maximize the received energy and satisfy spectral efficiency (SE) quality-of-service constraints. Practical system impairments, including a non-linear harvested energy model, pilot contamination (PC), channel estimation errors, and reliance on long-term statistical channel state information (CSI), are considered. We derive closed-form expressions for both the achievable SE and the average sum harvested energy (sum-HE). A mixed-integer non-convex optimization problem is formulated to jointly optimize the SIM phase shifts, APs mode selection, and power allocation to maximize average sum-HE under SE and average harvested energy constraints. To solve this problem, we propose a centralized training, decentralized execution (CTDE) framework based on deep reinforcement learning (DRL), which efficiently handles high-dimensional decision spaces. A Markovian environment and a normalized joint reward function are introduced to enhance the training stability across on-policy and off-policy DRL algorithms. Additionally, we provide a two-phase convex-based solution as a theoretical robust performance. Numerical results demonstrate that the proposed DRL-based CTDE framework achieves SWIPT performance comparable to convexification-based solution, while significantly outperforming baselines.

In terahertz (THz) communication systems, extremely large scale arrays can effectively compensate for the limited communication distance problem. In this article, we consider the near-field channel estimation (CE) problem for an extremely large reconfigurable intelligence surface (XL-RIS)-assisted multi-user THz communication system. We first construct a near-field channel model based on a second-order Fresnel approximation derivation. Utilizing the spatial structure of the derived channel model, we sample the covariance matrix of the received signals. Then, we propose a tensor decomposition-based algorithm to estimate the angular parameters, and establish a truncated singular value decomposition (T-SVD) algorithm for the distance estimation. In the end, we estimate the path losses through the least squares (LS) method and recover the complete channel. Moreover, to further reduce the computational overhead, we construct a low-complexity tensor completion-based scheme for the angular parameters’ estimation. Simulation results indicate that the proposed tensor-based CE schemes outperform the conventional subspace-based approaches in terms of accuracy and computational complexity.

We propose a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) architecture with dynamically switched active and passive elements to support joint localization, communication, and wireless power transfer (WPT). We first pursue a parallel factor analysis with the alternating least squares (PARAFAC-ALS)-based tensor decomposition approach that decouples the base station (BS)-reconfigurable intelligent surface (RIS) and RIS-user channels, thereby enabling low-overhead channel acquisition. Based on this, we formulate a system energy efficiency (EE) maximization problem, subject to the spectral efficiency (SE) requirements of communication users, sensing signal-to-interference-plus-noise ratio constraints, and the nonlinear energy harvesting requirements of energy-harvesting users. The optimization problem is nonconvex since the transmit power allocation, STAR-RIS coefficients, and active/passive mode assignments are tightly coupled in both the objective and constraints. We address this issue by alternating between two subproblems, and solving them via fractional programming, successive convex approximation and a multi-seed greedy strategy employed as an initialization step. Numerical results demonstrate that selectively activating a small, well-chosen subset of STAR-RIS elements achieves 1.5 to 3 times EE improvements compared with fully passive/active architectures, while satisfying communication, sensing, and power-transfer requirements.

Reconfigurable intelligent surface (RIS)-enabled systems have been widely considered as one of the revolutionary technologies for the new generation of communications. In particular, RIS-assisted multiple-input multiple-output (MIMO) communications have come at the forefront of research, yet there is still lack of supportive channel measurements and modeling in real environments. Against this background, this paper conducts multi-frequency and multi-scenario channel measurements and channel modeling for RIS-assisted MIMO communication systems. Utilizing a temporal autocorrelation-based channel sounder and the fabricated RISs, the virtual RIS-assisted MIMO channels are realized by successively moving the transceiver antennas on movable rotary tables. The channel realizations are collected in indoor hotspot (InH) and urban microcellular (UMi) scenarios at sub-6 GHz and millimeter-wave (mmWave) frequency bands, respectively, where various communication states, different coding schemes of the RIS, as well as with and without RIS deployment are fully considered. Based on the measured channel realizations, critical channel metrics including the signal power, effective rank, spectral efficiency (SE), root-mean-square delay-spread (RMS DS), RiceanK-factor (KF), spatial correlation, etc., are illustrated and compared under different coding schemes, communication states, deployment scenarios, and frequency bands. The measurement results indicate that the coding scheme of the RIS significantly impacts the channel performance, while the capability of RIS to customize the channel is strongly related to the power intensity it provides. Deploying an RIS with energy-focused coding can provide significant signal power gains for non-line-of-sight (NLoS) links and spatial multiplexing gains for line-of-sight (LoS) and obstructed-line-of-sight (OLoS) links, thereby greatly improving the SE. Moreover, it is found that under different communication states, such as RIS-assisted NLoS/LoS/OLoS links, the influence imposed by RIS on the channel metrics could be completely opposite, especially for the KF, RMS DS, and effective rank. In addition, after the RIS deployment with a beamforming mode, the spatial correlation in the NLoS and LoS MIMO channels significantly increases and decreases, respectively. Furthermore, it is verified that the Weichselberger model can provide a satisfactory prediction accuracy on the SE of RIS-assisted MIMO channels, while the Kronecker model underestimates it.

This paper presents channel measurement campaigns and spatial channel characterization of reconfigurable intelligent surface (RIS)-assisted millimeter-wave multiple-input multiple-output (MIMO) systems. Utilizing a channel sounder and an RIS, the RIS-assisted MIMO channels are constructed in an indoor non-line-of-sight scenario. By rotating a narrow-beam directional antenna (DA) in the azimuth angle domain, the spatial signal distributions are captured. Meanwhile, multiple comparative experiments, including: wideband vs narrowband (NB) and DA vs omnidirectional antenna, are conducted. The influence of RIS deployment and different coding schemes is considered. Based on such channel realizations, spatial channel metrics, including the power azimuth spectrum (PAS), root mean square angular spread (RMS AS), root mean square delay spread (RMS DS), spatial correlation coefficient (SCC), effective rank, and etc., are thoroughly investigated. Measurement results show that, using the DA, NB signal, and beamforming provided by the RIS contributes to a lower RMS DS, a higher SCC, and a lower effective rank. A truncated Laplacian function is used to describe the PAS, indicating prominent signal strength improvements in both the primary direction facing RIS and the opposite direction to RIS. The RMS ASs are well-fitted by a generalized extreme value distribution, which manifest a distance-dependent variation in the space domain.

This paper studies a cell-free massive multiple-input multiple-output (CF-mMIMO) proactive monitoring system in which multiple multi-antenna monitoring nodes (MNs) are assigned to either observe the transmissions from an untrusted transmitter (UT) or to jam the reception at the untrusted receiver (UR). We propose an effective channel state information (CSI) acquisition scheme for the monitoring system. In our approach, the MNs leverage the pilot signals transmitted during the uplink and downlink phases of the untrusted link and estimate the effective channels corresponding to the UT and UR via a minimum mean-squared error (MMSE) estimation scheme. We derive new spectral efficiency (SE) expressions for the untrusted link and the monitoring system. For the latter, the SE is derived for two CSI availability cases at the central processing unit (CPU); namely case-1: imperfect CSI knowledge at both MNs and CPU, case-2: imperfect CSI knowledge at the MNs and no CSI knowledge at the CPU. To improve the monitoring performance, we propose a novel joint mode assignment and jamming power control optimization method to maximize the monitoring success probability (MSP) based on the Bayesian optimization framework. Numerical results show that (a) our CF-mMIMO proactive monitoring system relying on the proposed CSI acquisition and optimization approach significantly outperforms the considered benchmarks; (b) the MSP performance of our CF-mMIMO proactive monitoring system is greater than 0.8, regardless of the number of antennas at the untrusted nodes or the precoding scheme for the untrusted transmission link.

Integrated sensing and communication (ISAC) systems are key enablers of future networks but raise significant security concerns. In this realm, the emergence of malicious ISAC systems has amplified the need for authorized parties to legitimately monitor suspicious communication links and protect legitimate targets from potential detection or exploitation by malicious foes. In this paper, we propose a new wireless proactive monitoring paradigm, where a legitimate monitor intercepts a suspicious communication link while performing cognitive jamming to enhance the monitoring success probability (MSP) and simultaneously safeguard the target. To this end, we derive closed-form expressions of the signal-to-interference-plus-noise-ratio (SINR) at the user (UE), sensing access points (S-APs), and an approximating expression of the SINR at the proactive monitor. Moreover, we propose an optimization technique under which the legitimate monitor minimizes the success detection probability (SDP) of the legitimate target, by optimizing the jamming power allocation over both communication and sensing channels subject to total power constraints and monitoring performance requirement. To enhance the monitor’s longevity and reduce the risk of detection by malicious ISAC systems, we further propose an adaptive power allocation scheme aimed at minimizing the total transmit power at the monitor while meeting a pre-selected sensing SINR threshold and ensuring successful monitoring. Our numerical results show that the proposed algorithm significantly compromises the sensing and communication performance of malicious ISAC.

Accurate acquisition of channel state information (CSI) is essential for fully harnessing the potential of stacked intelligent metasurfaces (SIMs) in communication systems. In this paper, we address the channel estimation (CE) problem in SIM-based multi-user (MU) millimeter-wave (mmWave) near-field communication systems. To address the severe path loss and blockage in mmWave communication systems, many meta-atoms are typically integrated into each layer of the SIM. Then, the number of radio frequency (RF) chains at the base station (BS) is fewer than that of meta-atoms per layer, resulting in an underdetermined problem. Additionally, the increase in the number of meta-atoms in each layer expands the SIM’s near-field region, leading to the user equipment (UEs) being mostly situated in this region, necessitating precise modeling of the channel under the spherical wavefront assumption. To address these issues, we introduce a compressed sensing (CS)-based CE protocol to tackle the underdetermined problem. In contrast to the traditional CS-based estimation framework, we investigate a polar-domain channel representation to tackle the severe energy spread effect of the classical angular-domain channel representation in near-field communication systems. Specifically, we design a novel polar-domain transform matrix for uniform planar arrays (UPAs), thereby transforming the CE problem into a sparse recovery task of the paths’ support set and complex gains. To overcome the limitations of the sparse Bayesian learning (SBL) framework in tackling high-dimensional dictionaries, we propose a low-complexity polar-domain SBL (LCPD-SBL) algorithm, which significantly reduces computational complexity without compromising estimation accuracy. Numerical simulation results demonstrate that the proposed polar-domain transform matrix yields a better estimation accuracy than traditional angular-domain approaches. Additionally, the proposed LCPD-SBL algorithm can be faster than existing SBL methods by up to 4× while sustaining the same estimation performance.

This paper presents an application of differential evolution in optimizing the exploitation of full-duplex communication for Cell-Free Massive Multiple Input Multiple Output (CF-mMIMO), a potential candidate for 6G networks. This paper proposes a new dynamic network-assisted full-duplex CF-mMIMO network, where access points can operate in either half-duplex or full-duplex mode, and each full-duplex access point can serve uplink and downlink users simultaneously. A long-term total spectral efficiency maximization problem is formulated subject to a network operation model and individual spectral efficiency requirements with a limited power budget. Due to the intrinsic nonconvexity and infeasible circumstances where some users might not achieve the rate requirements, we adapt differential evolution to design a low computational complexity algorithm, attaining good power allocation and network operation mode in polynomial time. We further analytically investigate the number of generations required to reach the optimal solution. Numerical results demonstrate the effectiveness of our system design and proposed algorithm over state-of-the-art benchmarks. The network can offer satisfactory service to most users, although several may be unscheduled under harsh conditions.

Non-orthogonal multiple access (NOMA) offers enhanced energy and spectral efficiency through effective resource sharing, yet uplink NOMA suffers from bit error rate (BER) degradation due to error propagation from imperfect successive interference cancellation (SIC). This paper investigates the potential BER performance enhancement via reconfigurable intelligent surfaces (RISs) in uplink NOMA systems. A novel RIS-assisted design is proposed, wherein the RIS phase shifts are optimized to amplify the received signals and mitigate channel-induced phase distortions. An accurate effective statistical channel modeling is developed to facilitate our closed-form BER analysis, supported by a two-user channel alignment scheme for efficient SIC detection. Based on the derived BER expressions, an optimized power allocation (PA) strategy is formulated to minimize the average BER under transmit power constraints. Simulation results validate the theoretical analysis, demonstrating that the proposed PA scheme effectively eliminates the BER floors associated with uplink NOMA at the base station.

This paper investigates an integrated sensing and communication (ISAC) system employing delay-Doppler (DD) signaling. The sensing performance of both random and deterministic signaling schemes is evaluated based on the expected squared ambiguity function (AF), for which closed-form expressions are derived by leveraging the Zak transform-based orthogonal time-frequency space (Zak-OTFS) modulation framework. Our analysis highlights a key difference between the two signaling types: DD domain ISAC (DD-ISAC) with deterministic signaling yields a roughly periodic AF with prominent peaks and low sidelobes between adjacent peaks, whereas DD-ISAC with random signaling using a Quadrature Phase-Shift Keying (QPSK) constellation exhibits low sidelobe values periodically without prominent peaks. Furthermore, we demonstrate that DD-ISAC enables a flexible trade-off between delay and Doppler sidelobe levels by adjusting the number of delay and Doppler bins. The analytical findings are explicitly validated through numerical simulations.

Sensing, as an underlying function of integrated sensing and communication (ISAC), can, in theory, enable numerous applications, including detection, localization, navigation, etc. However, in sixth generation (6G) and beyond, sensing data could be used in a more effective manner, while environmental mapping is a promising candidate to enhance the sensing capacity. This paper augments the conventional ISAC framework by introducing the concept of integrated localization, mapping, and communication (LMAC), exploring the feasibility of providing mapping services while maintaining localization accuracy. Closedform expressions for the communication and localization signal-to-interference-plus-noise ratios (SINRs) are analytically derived to evaluate both the communication performance and the localizability of the localization user and scatterers. Furthermore, the Cramér-Rao lower bounds (CRLBs) for localization and mapping services are provided to characterize the fundamental limits of an LMAC system. Numerical results indicate that the proposed performance bounds effectively characterize the system performance and offer valuable insights into how different network configurations influence the performance and realizable potential of LMAC.

We consider a single-user (SU) continuous fluid antenna system (CFAS) employing matched filtering (MF) operating over a Ricean fading channel. Focusing on the upper tail of the received signal-to-noise ratio (SNR) distribution (the high SNR probability (HSP)), we derive accurate approximations for the HSP in 1, 2, and 3 dimensions using the expected Euler characteristic (EEC), presenting the first analytical results for a CFAS in a Ricean environment. In the process, we provide the first closed-form expression for the Euler characteristic density of a non-central $\chi _2^2$ random field. We then examine the impact of the Ricean K-factor on the CFAS performance, emphasizing the critical role of channel variations in achieving a strong HSP.

With the substantial increase in the number of antennas, polar-domain channel modeling for extremely large-scale antenna array (ELAA) systems has been introduced to capture both angular and distance information in near-field environments. The fine-grained polar-domain channel provides additional sources of randomness, making it well-suited for physical layer key generation (PLKG). To minimize the pilot overhead in multi-user key generation and leverage the randomness from the polar-domain channel paths, we herein design a zero-forcing (ZF)-based precoding scheme to mitigate inter-path and inter-user interference. Using ZF precoding, we derive an analytical expression for the sum secret key rate (SKR) as a function of power allocation variables, and then optimize these variables in the presence of eavesdroppers. Our simulations validate the proposed precoding design and power allocation methods in terms of sum SKR versus the transmit power, antenna configurations, and spatial correlation between legitimate and eavesdropping channels.

In this paper, we propose a novel cross-domain channel estimation (CDCE) algorithm for orthogonal frequency division multiplexing (OFDM) systems, leveraging the unique characteristics of the delay-Doppler (DD) domain channel. Specifically, the proposed algorithm transforms the time-frequency (TF) domain pilot sequence of OFDM into the DD domain and applies a two-dimensional (2D) twisted-convolution for acquiring a coarse estimation of the underlying channel delay and Doppler. Then, the OFDM channel estimation is formulated as a sparse signal recovery problem in the TF domain according to the dictionary derived based on the obtained delay and Doppler estimates. Furthermore, a low-complexity ℓ1-regularized least-square estimator is proposed to effectively solve this problem. Moreover, we further develop a performance analysis framework of the proposed scheme based on the ambiguity function (AF) of the adopted pilot sequence. Our numerical results demonstrate noticeable estimation performance improvement compared to conventional OFDM channel estimation methods, particularly in the presence of high channel mobility.

Stacked intelligent metasurfaces (SIMs), emerging as a revolutionary programmable electromagnetic architecture, have demonstrated unprecedented capabilities in manipulating wireless propagation environments. However, the existing research on SIM-aided downlink communication does not consider the fairness among users. Therefore, this paper studies a SIM-aided hybrid analog-digital system, which aims to fairly guarantee the communication quality of each user while minimizing the transmission power. The hybrid system avails of a SIM for enhancing the communication channel with digital precoding to effectively suppress the interference between users. To this end, we formulate a transmit power minimization problem under quality-of-service constraints, solved by an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that compared to conventional fully digital multiuser multiple-input single-output (MISO) systems, the proposed SIM-aided hybrid system requires 6.93 dBm less transmit power under the same signal-to-interference-plus-noise ratio (SINR) constraints for users. This work reveals the SIM’s powerful wave-based beamforming capabilities, providing effective solutions for energy-efficient networks with low hardware cost.

A continuous aperture array (CAPA)-based multicast communication system is investigated in this paper, where a base station (BS) employs a CAPA to serve a set of multicast users. Under a transmit power constraint, the problem of maximizing the system multicast spectral efficiency (SE) by designing the CAPA beamformer is formulated, where the involved beamformer is a continuous current density function across the CAPA surface. By introducing auxiliary variables, the non-convex multicast SE objective function is first transformed into a tractable form. Then, to address the reformulated problem, an efficient block coordinate descent (BCD)-based algorithm is developed. The CAPA beamforming design subproblem can be optimally solved via the Lagrangian dual method and the calculus of variations (CoV) theory. Numerical results demonstrate that the considered CAPA can significantly improve the multicast SE compared to a conventional spatially discrete array (SPDA).

In this paper, we exploit the cell-free massive multiple-input multiple-output (CF-mMIMO) architecture to design a physical-layer authentication (PLA) framework that can simultaneously authenticate multiple distributed users across the coverage area. Our proposed scheme remains effective even in the presence of active adversaries attempting impersonation attacks to disrupt the authentication process. Specifically, we introduce a tag-based PLA CF-mMIMO system, wherein the access points (APs) first estimate their channels with the legitimate users during an uplink training phase. Subsequently, a unique secret key is generated and securely shared between each user and the APs. We then formulate a hypothesis testing problem and derive a closed-form expression for the probability of detection for each user in the network. Numerical results validate the effectiveness of the proposed approach, demonstrating that it maintains a high detection probability even as the number of users in the system increases.

The rapid proliferation of devices brought about by the Internet of Things (IoT) has underpinned the inclusion of sensing capabilities in wireless systems. Therefore, integrated sensing and communication (ISAC) is emerging as a key use case for next-generation systems. The dual functionality of ISAC systems increases interference, which multiple-input multipleoutput (MIMO) arrays can mitigate through spatial diversity. However, achieving substantial diversity gains with MIMO requires large antenna arrays, which could amplify the system complexity. Fluid antennas (FAs) provide an efficient alternative, delivering comparable spatial gains without the need for massive arrays, making them a promising candidate for ISAC systems. In this paper, we propose a quality-of-service (QoS) aware powerefficient transceiver design for an FA-ISAC system. We jointly design the antenna position vector, transmit beamformers, radar signal and receive combiners to meet the sensing and communication performance constraints. Our results demonstrate a 2 dB improvement compared to conventional MIMO systems.

In this paper, we propose a hybrid beamforming design for multiple target detection in an orthogonal time frequency space (OTFS)-based integrated sensing and communication (ISAC) multiple-input multiple-output (MIMO) system. The proposed hybrid beamformer allows spatial separation of the beams for communication and sensing, thereby eliminating inter-beam interference (IBI), while reducing the number of required radio frequency (RF) chains. More specifically, in addition to the beams allocated for communication users, multiple beams are assigned for target scanning and detection. By applying a combiner to the received echo signals, information about the target's existence, along with its angular, range, and Doppler characteristics, can be directly obtained across different RF chains. To shed light on the system performance, we analyze the signal-to-interference-plusnoise ratio (SINR) and discuss the effect of the beamformer in the on-grid and off-grid cases, respectively. Our simulation results indicate that accurate sensing can be achieved with integer delay and Doppler indices; however, in the cases of fractional delay and Doppler, the sensing accuracy depends on the resolution of these parameters.

Compared to range- and angle-based localization, the importance of received signal strength (RSS)-based positioning is gradually diminishing in beyond 5G networks due to its limited localization accuracy, despite its simplicity. This paper explores the potential of utilizing non-line-of-sight (NLoS) RSS measurements to enhance the localization performance of RSS-based systems. Different from the conventional RSS-based localization, which relies solely on line-of-sight (LoS) RSS measurements, our findings reveal that the NLoS RSS also contains valuable locationrelated information for localization. A simple and efficient localization scheme that attains the Cramér-Rao lower bound (CRLB) performance is developed, accounting for measurement misalignment caused by different reflection paths. Furthermore, a tractable expression of the CRLB is analytically derived, which offers insights into how different network parameters, such as the path-loss decay and reflection loss, affect the fundamental limits of the proposed scheme.

We consider a reconfigurable intelligent surface (RIS) that can implement a phase rotation continuously over the whole surface rather than via a finite number of discrete elements. Such an RIS can be considered a design for future systems where advances in metamaterials make such an implementation feasible or as the limiting case where the number of elements in a traditional RIS increases in a given area. We derive the optimal RIS design for the single-user (SU) scenario assuming a line-ofsight (LoS) from the RIS to the base station (BS) and correlated Rayleigh fading for the other links. We also derive the associated optimal signal-to-noise ratio (SNR) and its mean, a bound on the mean spectral efficiency (SE), an approximation to the SNR outage probability and an approximation to the coefficient of variation for the investigation of channel hardening.

Long-range (LoRa) can provide highly energy-efficient and cost-effective communications for low power wide area networks, playing an indispensable role in the Internet of Things (IoT). However, terrestrial LoRa networks cannot guarantee pervasive connectivity, especially in rural and remote areas. To tackle this problem, exploiting LoRa-based low Earth orbit (LEO) satellite IoT has garnered a growing interest in both academia and industry. In this paper, we provide a novel analytical framework based on spherical stochastic geometry (SG) for characterizing the uplink access probability of LoRa-based LEO satellite IoT. For practical modeling, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of near-Earth satellite communications and the unique features of LoRa network are considered to derive closed-form analytical expressions for the uplink access probability. Numerical simulations validate the accuracy of our theoretical analysis and provide insightful guidelines for the practical design and implementation of LoRa-based LEO satellite IoT.

This paper investigates the problem of localizing and associating multiple targets in an integrated sensing and communication (ISAC) system that employs a multi-static cell-free massive multiple-input multiple-output ($\mathbf{C F}-\mathbf{m M I M O}$) architecture. In this system, a large area is covered by a number of distributed access points (APs). The problem of simultaneously detecting and locating multiple targets is considered to be crucial and challenging, particularly in order to avoid interference in the communications functionalities. By using the virtual channel representation of both the sensing and communication channels and the angular estimation method, e.g., estimation of signal parameters via rotational invariance techniques (ESPRIT), we can first accurately identify specific angular directions from unidentified targets to each receiving (Rx)-AP. Then, we transform the association problem into a clustering problem. We propose a low-complexity approach based on the clustering algorithm to solve this association problem. The proposed method yields robust communication performance while simultaneously achieving outstanding association and localization performance.

Unmanned aerial vehicles (UAVs) are now being used to efficiently collect data from passive backscattering tags and support existing terrestrial links in the Internet of Things (IoT) when these links become overloaded. However, securing UAV-aided backscatter communication (BSC) in non-terrestrial networks is challenging due to the hardware limitations of passive tags. This paper introduces a secure BSC system utilizing a UAV for radio frequency (RF) signal transmission and data collection. Batteryless tags or backscatter devices (BDs) harness these RF signals to communicate with the UAV, even under untrusted scenarios where the BDs are mutually untrusted. We enhance the uplink fair-secrecy rate of the BDs by jointly optimizing the transmit and received beamforming vectors, artificial noise (AN), and power allocation while achieving the energy harvesting requirements and UAV flight constraints. Block coordinate descent (BCD) and fractional programming (FP) algorithms are used to address the non-convexity and obtain a fast converging solution. Simulation results verify the analysis, provide valuable insights, and demonstrate the substantial performance gains of our design for UAV-aided secure BSC in improving the fair-secrecy rate. Specifically, our proposed design achieves 0.19%, 2.08%, and 69.78% higher performance compared to three benchmarks.

This paper investigates near-field multi-user downlink communications over Ricean fading channels underpinned by the holographic multiple-input multiple-output (HMIMO) technology. We first establish the mutual coupling and radiation efficiency models to characterize the effect of mutual coupling and then formulate the practical input-output relationship. Based on this, the achievable spectral efficiency (SE) is derived for maximum ratio transmission (MRT). By further investigating the special cases of pure line-of-sight (LoS) and Rayleigh fading, our analysis reveals that for a moderate number of antenna elements, the system's SE with mutual coupling might outperform that without mutual coupling, especially in the low transmit power regime. Moreover, the additional distance degrees-of-freedom (DoF) introduced by the near-filed channel can enable the inter-user interference mitigation, even for the worst case when the users have similar angular directions. Finally, the obtained theoretical analysis is validated through simulations.

Joint unicast and multicast transmissions are becoming increasingly important in practical wireless systems, such as Internet of Things networks. This paper investigates a cell-free massive multiple-input multiple-output system that simultaneously supports both transmission types, with multicast serving multiple groups. Exact closed-form expressions for the achievable downlink spectral efficiency (SE) of both unicast and multicast users are derived for zero-forcing and maximum ratio precoding designs. Accordingly, a weighted sum SE (SSE) maximization problem is formulated to jointly optimize the access point (AP) selection and power allocation. The optimization framework accounts for practical constraints, including the maximum transmit power per AP, fronthaul capacity limitations between APs and the central processing unit, and quality-of-service requirements for all users. The resulting non-convex optimization problem is reformulated into a tractable structure, and an accelerated projected gradient (APG)-based algorithm is developed to efficiently obtain near-optimal solutions. As a performance benchmark, a successive convex approximation (SCA)-based algorithm is also implemented. Simulation results demonstrate that the proposed joint optimization approach significantly enhances the SSE across various system setups and precoding strategies. In particular, the APG-based algorithm achieves substantial complexity reduction while maintaining competitive performance, making it well-suited for large-scale practical deployments.

Recently, Long-Range (LoRa) based low Earth orbit (LEO) satellite Internet of Things (IoT) has garnered growing interest from both academia and industry, since it can guarantee pervasive connectivity in an energy-efficient and cost-effective manner. In this paper, we provide a novel spherical stochastic geometry (SG) based analytical framework for characterizing the uplink access probability of LoRa-based LEO satellite IoT system. Specifically, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of LoRa EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of the satellite-to-Earth communications and the unique features of the LoRa network are considered to derive closed-form analytical expressions for the uplink access probability of such a new paradigm. Moreover, the non-trivial impact of the spreading factor, the ED’s density, the orbit altitude, and the satellite effective beamwidth on the system performance is thoroughly investigated. Extensive numerical simulations are conducted, which not only validate the accuracy of our theoretical analysis but also provide useful insights into the practical design and implementation of LoRa-based LEO satellite IoT system.

This paper proposes a joint active and passive beamforming design for reconfigurable intelligent surface (RIS)-aided wireless communication systems, adopting a piecewise near-field channel model. While a traditional near-field channel model, applied without any approximations, offers higher modeling accuracy than a far-field model, it renders the system design more sensitive to channel estimation errors (CEEs). As a remedy, we propose to adopt a piecewise near-field channel model that leverages the advantages of the near-field approach while enhancing its robustness against CEEs. Our study analyzes the impact of different channel models, including the traditional near-field, the proposed piecewise near-field and far-field channel models, on the interference distribution caused by CEEs and model mismatches. Subsequently, by treating the interference as noise, we formulate a joint active and passive beamforming design problem to maximize the spectral efficiency (SE). The formulated problem is then recast as a mean squared error (MSE) minimization problem and a suboptimal algorithm is developed to iteratively update the active and passive beamforming strategies. Simulation results demonstrate that adopting the piecewise near-field channel model leads to an improved SE compared to both the near-field and far-field models in the presence of CEEs. Furthermore, the proposed piecewise near-field model achieves a good trade-off between modeling accuracy and system’s degrees of freedom (DoF).

Orthogonal time frequency space (OTFS) modulation has received significant attention recently due to its superior performance compared to conventional multicarrier waveforms. However, symbol detection with OTFS is significantly more involved and typically operates on large signal blocks with intersymbol interference (ISI) in the delay-Doppler (DD) domain. In this paper, we investigate the performance of OTFS within the cross-domain iterative detection (CDID) framework. Specifically, three distinct CDID algorithms are presented and investigated, which estimate/detect the information symbols iteratively across the frequency and DD domains via passing either thea posteriorior extrinsic information using a full-sized or single-tap linear minimum mean square error (LMMSE) estimator. Building upon this framework, we study the average mean square error (MSE) for the considered CDID algorithms, where both the bias evolution and the state (variance) evolution are investigated. Particularly, we show that the proposed CDIDs can provide unbiased estimation under certain channel conditions. Furthermore, a fixed point exists in the state evolution when the estimation is unbiased, indicating that the algorithm’s convergence is guaranteed. More importantly, we reveal that passing thea posterioriinformation is more beneficial when the underlying channel has negligible Doppler spread while passing the extrinsic information is more suitable for non-negligible Doppler spread cases, where the frequency domain channel matrix lacks diagonal dominance. Our numerical results confirm our analytical findings and unveil the near-optimal error performance achieved by the proposed design.

We consider a downlink (DL) massive multiple-input multiple-output (MIMO) system, where different users have different mobility profiles. To support this system, we categorize the users into two disjoint groups according to their mobility profile and implement a hybrid orthogonal time frequency space (OTFS)/orthogonal frequency division multiplexing (OFDM) modulation scheme. Building upon this framework, two precoding designs, namely full-pilot zero-forcing (FZF) precoding and partial zero-forcing (PZF) precoding are considered. To shed light on the system performance, the spectral efficiency (SE) with a minimum-mean-square-error (MMSE)-successive interference cancellation (SIC) detector is investigated. Closed-form expressions for the SE are obtained using some tight mathematical approximations. To improve fairness among different users, we consider max-min power control for both precoding schemes based on the closed-form SE expression. However, by noting the large performance gap for different groups of users with PZF precoding, the per-user SE will be compromised when pursuing overall fairness. Therefore, we propose a weighted max-min power control scheme. By introducing a weighting coefficient, the trade-off between the per-user performance and fairness can be enhanced. Our numerical results confirm the theoretical analysis and reveal that with mobility-based grouping, the proposed hybrid OTFS/OFDM modulation significantly outperforms the conventional OFDM modulation for high-mobility users.

Cell-free (CF) architectures and full-duplex (FD) communication are leading candidates for next-generation wireless networks. The CF framework removes cell boundaries in traditional cell-based systems, thereby mitigating the inter-cell interference and improving the coverage probability. In contrast, FD communication allows simultaneous transmission and reception on the same frequency-time resources, effectively doubling the spectral efficiency (SE). The integration of these technologies, known as CF FD communication, leverages the advantages of both approaches to enhance the spectral and energy efficiency in wireless networks. CF FD communication is particularly promising due to the low-power and cost-effective FD-enabled access points (APs), which are ideal for short-range transmissions between APs and users. Despite its potential, a comprehensive survey or tutorial on CF FD communication has been notably absent. This paper aims to address this gap in the literature. It begins with an overview of FD communication fundamentals, self-interference cancellation techniques, and CF technology principles, including their implications for current wireless networks. The discussion then moves to the integration and compatibility of CF and FD technologies, focusing on channel estimation, performance analysis, and resource allocation in CF FD massive multiple-input multiple-output (mMIMO) networks, supported by an extensive literature review and case studies. The potential of combining a sub-category of CF architecture—network-assisted CF technology—with FD technology is also explored, including a detailed case study on fundamentals, performance analysis, AP operation, and mode assignments. Finally, emerging CF FD paradigms, like millimeter-wave communications, unmanned aerial vehicles, and reconfigurable intelligent surfaces, are discussed, highlighting existing contributions and unresolved issues.

We investigate the integration of beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) into cell-free massive multiple-input multiple-output (CF-mMIMO) systems to enhance simultaneous wireless information and power transfer (SWIPT). To simultaneously support two groups of users—energy receivers (ERs) and information receivers (IRs)— without sacrificing time-frequency resources, a subset of access points (APs) is dedicated to serving ERs with the aid of a BD-RIS, while the remaining APs focus on supporting IRs. A protective partial zero-forcing precoding technique is implemented at the APs to manage the non-coherent interference between the ERs and IRs. Subsequently, closed-form expressions for the spectral efficiency of the IRs and the average sum of harvested energy (HE) at the ERs are leveraged to formulate a comprehensive optimization problem. This problem jointly optimizes the AP selection, AP power control, and scattering matrix design at the BD-RIS, all based on long-term statistical channel state information. This challenging problem is then effectively transformed into more tractable forms. To solve these sub-problems, efficient algorithms are proposed, including a heuristic search for the scattering matrix design, as well as successive convex approximation and deep reinforcement learning methods for the joint AP mode selection and power control design. Numerical results show that a BD-RIS with a group- or fully-connected architecture achieves significant EH gains over the conventional diagonal RIS, especially delivering up to a 7-fold increase in the average sum of HE when a heuristic-based scattering matrix design is employed.

In this paper, we investigate the channel estimation and user localization problems for multi-user integrated sensing and communication (ISAC) systems empowered by the reconfigurable intelligent surface (RIS) technology. In order to perceive environmental information more deeply, we propose a spherical RIS architecture with spherically arranged unit cells. Based on the principle of phase mode excitation, we customize the design of RIS profiles and recover the equivalent channel parameters via subspace estimation tools. By exploring the characteristics of RIS array manifold and free-space propagation, we develop a decoupling framework of three-dimensional channel parameters, which is not supported by conventional planar RIS topologies. Each user can achieve a self-localization by analyzing the signals transmitted from other active users. Simulation results indicate that the spherical RIS can enable joint channel estimation, user localization and data transmission with remarkable performance that approaches the theoretical Cramér-Rao bounds.

In this paper, we investigate the channel estimation and localization problems for integrated sensing and communication (ISAC) systems empowered by the reconfigurable intelligent surface (RIS) technology. We propose a cylindrical RIS architecture that arranges reflecting elements on a curved substrate, where the three-dimensional array manifold can not only offer a 360° coverage but also perceive the environmental information more deeply. The conformal RIS topology can fit the deployment scenarios more flexibly, which, however, incurs a potential issue of shadowing effect, i.e., signal waves from/to certain directions can only be observed by a part of reflectors due to the shielding of the substrate curvature, yielding different visibility regions (VRs) for multiple users on the RIS array manifold. In order to address this problem, we propose a tensorial channel estimation approach, where the cascaded channel is transformed into the beamspace domain and modeled as a canonical polyadic tensor. By leveraging the principle of tensor completion, we can eliminate the RIS training profiles to deconstruct the channel in the element domain. Then, we develop a VR detection strategy based on the sliding windows, retrieving equivalent channel parameters from the effective signal responses. Finally, by exploring the characteristics of the cylindrical RIS architecture, we develop a decoupling framework to uniquely recover the exact channel parameters, based on which each user can locate itself and other interacting ones. Simulation results indicate that the proposed cylindrical RIS can enable the channel estimation, user localization and data transmission simultaneously, exhibiting remarkable performance under the shadowing effect interference.

We present an overview of ongoing research endeavors focused on in-band full-duplex (IBFD) massive multiple-input multiple-output (MIMO) systems and their applications. In response to the unprecedented demands for mobile traffic in concurrent and upcoming wireless networks, a paradigm shift from conventional cellular networks to distributed communication systems becomes imperative. Cell-free massive MIMO (CF-mMIMO) emerges as a practical and scalable implementation of distributed/network MIMO systems, serving as a crucial physical layer technology for the advancement of next-generation wireless networks. This architecture inherits benefits from co-located massive MIMO and distributed systems and provides the flexibility for integration with the IBFD technology. We delineate the evolutionary trajectory of cellular networks, transitioning from conventional half-duplex multi-user MIMO networks to IBFD CF-mMIMO. The discussion extends further to the emerging paradigm of network-assisted IBFD CF-mMIMO (NAFD CF-mMIMO), serving as an energy-efficient prototype for asymmetric uplink and downlink communication services. This novel approach finds applications in dual-functionality scenarios, including simultaneous wireless power and information transmission, wireless surveillance, and integrated sensing and communications. We highlight various current use case applications, discuss open challenges, and outline future research directions aimed at fully realizing the potential of NAFD CF-mMIMO systems to meet the evolving demands of future wireless networks.

We investigate reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) Internet of Things (IoT) networks, where energy-limited IoT devices are overlaid with cellular information users (IUs). IoT devices are wirelessly powered by a RIS-assisted massive multiple-input multiple-output (MIMO) base station (BS), which is simultaneously serving a group of IUs. By leveraging a two-timescale transmission scheme, precoding at the BS is developed based on the instantaneous channel state information (CSI), while the passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. We derive closed-form expressions for the achievable spectral efficiency of the IUs and average harvested energy at the IoT devices, taking the channel estimation errors and pilot contamination into account. Then, a non-convex max-min fairness optimization problem is formulated subject to the power budget at the BS and individual quality of service requirements of IUs, where the transmit power levels at the BS and passive RIS reflection coefficients are jointly optimized. Our simulation results show that the average harvested energy at the IoT devices can be improved by 132% with the proposed resource allocation algorithm. Interestingly, IoT devices benefit from the pilot contamination, leading to a potential doubling of the harvested energy in certain network configurations.

The core idea of holographic MIMO (HMIMO) is to densely deploy numerous antenna elements within a given aperture size. However, with the denser distribution of antenna elements, stronger mutual coupling effects would kick in among antenna elements, which would eventually affect the communication performance. Meanwhile, as the holographic array usually has large physical size, the possibility of near-field communication increases. This paper investigates a near-field multi-user downlink HMIMO system and characterizes the spectral efficiency (SE) under the mutual coupling effect over Ricean fading channels. Both perfect and imperfect channel state information (CSI) scenarios are considered. (i) For the perfect CSI case, the mutual coupling and radiation efficiency model are first established. Then, a closed-form SE expression is derived under maximum ratio transmission (MRT). By comparing the SE between the cases with and without mutual coupling, it is unveiled that the system SE with mutual coupling might outperform that without mutual coupling in the low transmit power regime for a given aperture size. Moreover, it is also unveiled that the inter-user interference cannot be eliminated unless the physical size of the array increases to infinity. Fortunately, the additional distance term in the near-field channel can be exploited for the inter-user interference mitigation, especially for the worst case, where the users’ angular positions overlap to a great extent. (ii) For the imperfect CSI case, the channel estimation error is considered for the derivation of the closed-form SE under MRT. It shows that in the low transmit power regime, the system SE can be enhanced by increasing the pilot power and the antenna element density, the latter of which will lead to severe mutual coupling. In the high transmit power regime, increasing the pilot power has a limited effect on improving the system SE. However, increasing the antenna element density remains highly beneficial for enhancing the system SE. Finally, both analytical and simulation results confirm that reducing the antenna spacing will be accompanied by significant mutual coupling effects, which may potentially enhance the system SE. However, this enhancement is ultimately limited by the radiation efficiency of the antennas and the physical size of the array.

We propose reflection pattern modulation-aided reconfigurable intelligent surface (RPM-RIS)-assisted cell-free massive multiple-input-multiple-output (CF-mMIMO) schemes for green uplink transmission. In our RPM-RIS-assisted CF-mMIMO system, extra information is conveyed by the indices of the active RIS blocks, exploiting the joint benefits of both RIS-assisted CF-mMIMO transmission and RPM. Since only part of the RIS blocks are active, our proposed architecture strikes a flexible energy vs. spectral efficiency (SE) trade-off. We commence with introducing the system model by considering spatially correlated channels. Moreover, we conceive a channel estimation scheme subject to the linear minimum mean-square error (MMSE) constraint, yielding sufficient information for the subsequent signal processing steps. Then, upon exploiting a so-called large-scale fading decoding (LSFD) scheme, the uplink signal-to-interference-and-noise ratio (SINR) is derived based on the RIS ON/OFF statistics, where both maximum ratio (MR) and local minimum mean-square error (L-MMSE) combiners are considered. By invoking the MR combiner, the closed-form expression of the uplink SE is formulated based only on the channel statistics. Furthermore, we derive the total energy efficiency (EE) of our proposed RPM-RIS-assisted CF-mMIMO system. Additionally, we propose a chaotic sequence-based adaptive particle swarm optimization (CSA-PSO) algorithm to maximize the total EE by designing the RIS phase shifts. Specifically, the initial particle diversity is promoted by invoking chaotic sequences, and an adaptive time-varying inertia weight is developed to improve its particle search performance. Furthermore, the particle mutation and reset steps are appropriately selected to enable the algorithm to escape from local optima. Finally, our simulation results demonstrate that the proposed RPM-RIS-assisted CF-mMIMO architecture strikes an attractive SE vs. EE trade-off, while the CSA-PSO algorithm is capable of attaining a significant EE performance gain compared to conventional solutions.

Reconfigurable massive multiple-input multiple-output (RmMIMO) technology, as an electronically-controlled fluid antenna system, offers increased flexibility for future communication systems by exploiting previously untapped degrees of freedom in the electromagnetic (EM) domain. The representation of the traditional spatial domain channel state information (sCSI) limits the insights into the potential of EM domain channel properties, constraining the base station’s (BS) utmost capability for precoding design. This paper leverages the EM domain channel state information (eCSI) for antenna radiation pattern design at the BS. We develop an orthogonal decomposition method based on spherical harmonic functions to decompose the radiation pattern into a linear combination of orthogonal bases. By formulating the radiation pattern design as an optimization problem for the projection coefficients over these bases, we develop a manifold optimization-based method for iterative radiation pattern and digital precoder design. To address the eCSI estimation problem, we capitalize on the inherent structure of the channel. Specifically, we propose a subspace-based scheme to reduce the pilot overhead for wideband sCSI estimation. Given the estimated full-band sCSI, we further employ parameterized methods for angle of arrival estimation. Subsequently, the complete eCSI can be reconstructed after estimating the equivalent channel gain via the least squares method. Simulation results demonstrate that, in comparison to traditional mMIMO systems with fixed antenna radiation patterns, the proposed RmMIMO architecture offers significant throughput gains for multi-user transmission at a low channel estimation overhead.

Thanks to the ability to customize the propagation of wireless signals, reconfigurable intelligent surfaces (RISs) have great potential in enhancing the performance of future wireless communication systems. While the majority of papers in the literature considers single-RIS scenarios, the potential deployment of multiple RISs, that offer ubiquitous connectivity for diverse user demands, calls for further investigation. This paper considers a downlink multi-group multicast system underpinned by multiple RISs and aims to maximize the sum spectral efficiency subject to an overall transmit power constraint. This optimization problem is highly challenging due to the non-convex, non-smooth, and non-differentiable properties of the objective function, as well as the non-convex unit modulus constraint. To address this complex problem, we propose a model-driven deep learning (DL) approach. This involves first solving the joint active and passive beamforming design through an alternating projected gradient (APG) algorithm with an approximate objective function. The APG algorithm is then unfolded into an iterative procedure using multiple layers with trainable parameters. A network training method is proposed to ensure that the performance improves with the number of iterations. Remarkably, our model is also nicely generalizable to the imperfect channel state information (CSI) scenario, without any change to the network architecture, by simply combining the recursive approximation method and adding some long/short-term trainable parameters to accommodate the two-timescale transmission protocol. Our simulation results demonstrate the superiority of our proposed DL method over existing algorithms in terms of both complexity and performance. Specifically, the proposed model-driven DL method reduces the runtime by approximately 80% compared to the APG algorithm and 99.97% compared to the majorization-minimization algorithm, while it also achieves comparable performance. Furthermore, our proposed method for imperfect CSI scenarios reduces the performance loss by 5%-10% compared to the proposed method without considering the influence of imperfect CSI.

We propose a distributed implementation of integrated sensing and communication (ISAC) underpinned by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations’ requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via a successive convex approximation approach. We further propose a low-complexity design, where the AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of 100% for different network setups with a satisfactory fairness among all UEs.

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems that underpin simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. We propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. The performance of the system, from both a spectral efficiency (SE) and energy efficiency (EE) perspective, is comprehensively analyzed. Specifically, we formulate two mixed-integer nonconvex optimization problems for maximizing the average sum-SE and EE, under realistic power consumption models and constraints on the minimum individual SE requirements for individual IUs, minimum HE for individual EUs, and maximum transmit power at each AP. The challenging optimization problems are solved using successive convex approximation (SCA) techniques. The proposed framework design is further applied to the average sum-HE maximization and energy harvesting fairness problems. Our numerical results demonstrate that the proposed joint AP operation mode selection and power control algorithm can achieve EE performance gains of up to 4-fold and 5-fold over random AP operation mode selection, with and without power control respectively.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems are investigated under spatially correlated fading channels using realistic imperfect hardware. Specifically, the transceiver distortions, time-varying phase noise, and RIS phase shift errors are considered. Upon considering imperfect hardware and pilot contamination, we derive a linear minimum mean-square error (MMSE) criterion-based cascaded channel estimator. Moreover, a closed-form expression of the downlink ergodic spectral efficiency (SE) is derived based on maximum ratio (MR) based transmit precoding and channel statistics, where both a finite number of access points (APs) and STAR-RIS elements as well as imperfect hardware are considered. Furthermore, by exploiting the ergodic signal-to-interference-plus-noise ratios (SINRs) among user equipment (UE), a max-min fairness problem is formulated for the joint optimization of the passive transmitting and reflecting beamforming (BF) at the STAR-RIS as well as of the power control coefficients. An alternating optimization (AO) algorithm is proposed for solving the resultant problems, where iterative adaptive particle swarm optimization (APSO) and bisection methods are proposed for circumventing the non-convexity of the RIS passive BF and the quasi-concave power control sub-problems, respectively. Our simulation results illustrate that the STAR-RIS-aided CF-mMIMO system attains higher SE than its RIS-aided counterpart. The performance of different hardware parameters is also evaluated. Additionally, it is demonstrated that the SE of the worst UE can be significantly improved by exploiting the proposed AO-based algorithm compared to conventional solutions associated with random passive BF and equal-power scenarios.

Traditional centralized direct localization methods require the transmission of the complete baseband signal to the fusion center (FC) for target localization. Due to the limited communication bandwidth as well as energy required in transmission, this centralized framework is not suitable for largescale sensor networks. This paper proposes an information-driven decentralized direct localization framework. Firstly, a maximum-likelihood position estimator, based on the Gauss-Newton method, is derived. Then, a decentralized implementation framework is constructed. At its core, there is no dedicated FC while the sensors transmit information to their neighboring nodes only through single hops, achieving target localization through iterative processes based on the concept of consensus. Simulation results confirm the stability and robustness of the proposed method in different scenarios.

This paper explores a discrete energy state transition model for energy harvesting (EH) in cell-free massive multiple-input multiple-output (CF-mMIMO) networks. Multiple-antenna access points (APs) provide wireless power and information to single-antenna UE equipment (UEs). The harvested energy at the UEs is used for both uplink (UL) training and data transmission. We investigate the energy transition probabilities based on the energy differential achieved in each coherence interval. A Markov chain-based stochastic process is introduced to characterize the evolving UE energy status. A detailed statistical model is developed for a non-linear EH circuit at the UEs, using the derived closed-form expressions for the mean and variance of the harvested energy. More specifically, simulation results confirm that the proposed Gamma distribution approximation can accurately capture the statistical behavior of the harvested energy. Furthermore, the energy state transitions are evaluated using the proposed Markov chain-based framework, while mathematical expressions for the self, positive and negative transition probabilities of the discrete energy states are also presented. Our numerical results depict that increasing the number of APs with a constant number of service antennas provides significant improvement in the positive energy state transition and reduces the negative transition probabilities of the overall network.

We consider a joint orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) cell-free massive multiple-input multiple-output (CFmMIMO) system with limited fronthaul capacity. In this system, some users (UEs) are grouped to be served by access points (APs) in NOMA mode, while other UEs are served in OMA mode. We formulate a mixed-integer nonconvex problem of optimizing the power control and AP-group association to maximize the sum spectral efficiency (SE) in the considered system. This problem is subject to minimum SE requirements of each UE, per-AP transmit power, and limited fronthaul capacity. We propose an algorithm based on the successive convex approximation (SCA) optimization technique to obtain a stationary-point solution for the formulated problem. Numerical results demonstrate that the proposed joint optimization approach increases significantly sum SE compared to other heuristic baseline schemes, especially under a tight fronthaul capacity limitation. Also, the joint OMA-NOMA CFmMIMO system provides remarkably higher 95%-likelihood sum SE compared to a CFmMIMO system using only the OMA scheme, especially up to 42% when the coherence interval is short.

This paper considers a simultaneously transmitting and reflecting reconfigurable intelligent surface (STARRIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) system, accounting for imperfect hardware in spatially correlated fading channels. Specifically, we consider the hardware impairments and phase noise at transceivers, as well as the phase shift errors generated within the STAR-RIS. We commence by introducing the STAR-RIS signal model, channel model, and imperfect hardware components. Then, the linear minimum mean-square error (MMSE) channel estimate is derived with pilot contamination, which provides sufficient information for sequential data processing. Moreover, a channel capacity lower bound is derived in the case of a finite number of RIS elements and access points (APs), while a closed-form expression for the downlink ergodic spectral efficiency (SE) for maximum ratio (MR) precoding is also deduced, where only the channel statistics are used. Our numerical results demonstrate that the STAR-RIS-aided CF-mMIMO system achieves higher SE compared to the conventional CF-mMIMO system, even with imperfect hardware.

In this paper, we investigate the performance of the cross-domain iterative detection (CDID) framework with orthogonal time frequency space (OTFS) modulation, where two distinct CDID algorithms are presented. The proposed schemes estimate/detect the information symbols iteratively across the frequency domain and the delay-Doppler (DD) domain via passing either the a posteriori or extrinsic information. Building upon this framework, we investigate the error performance by considering the bias evolution and state evolution. Furthermore, we discuss their error performance in convergence and the DD domain error state lower bounds in each iteration. Specifically, we demonstrate that in convergence, the ultimate error performance of the CDID passing the a posteriori information can be characterized by two potential convergence points. In contrast, the ultimate error performance of the CDID passing the extrinsic information has only one convergence point, which, interestingly, aligns with the matched filter bound. Our numerical results confirm our analytical findings and unveil the promising error performance achieved by the proposed designs.

In this paper, we present the results of a set of channel measurements conducted within the 6 GHz band used in IEEE 802.11be based wireless local area networks (WLANs). A range of indoor and outdoor client to access point (AP) communication scenarios were considered for both line-of-sight (LOS) and non-LOS (NLOS) channel conditions. We have investigated the path loss, large-scale, and small-scale fading across 256 frequency points between 6.425 and 6.445 GHz. To model the large-scale fading we have utilized the lognormal and gamma distributions, while for the small-scale fading this was the Rayleigh, Rician, and Nakagami-m distributions. The information loss incurred when encoding the empirical distributions with the aforementioned theoretical ones was determined using the resistor-average distance (RAD). It was found that the gamma distribution provided a better fit to the large-scale fading, while the Rician and Nakagami-m distributions observed the lowest RAD values for the small-scale fading. To ascertain the temporal stability of the considered channels, the coherence time was inferred using an analysis of the autocorrelation. Our results indicate that the coherence time for the large-scale fading was typically longer than for small-scale fading.

In this paper, we investigate joint power control and AP selection scheme in a cell-free massive multiple-input multiple-output (CF-mMIMIO) system under an active eaves-dropping attack, where an eavesdropper tries to overhear the signal sent to one of the legitimate users by contaminating the uplink channel estimation. We formulate a joint optimization problem to minimize the eavesdropping spectral efficiency (SE) while guaranteeing a given SE requirement at legitimate users. The challenging formulated problem is converted into a more tractable form and an efficient low-complexity accelerated pro-jected gradient (APG)-based approach is proposed to solve it. Our findings reveal that the proposed joint optimization approach significantly outperforms the heuristic approaches in terms of secrecy SE (SSE). For instance, the 50% likely SSE performance of the proposed approach is 265 % higher than that of equal power allocation and random AP selection scheme.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a transformative technology to achieve spectral-efficient and energy-saving wireless communication. However, XL-MIMO leads to the near-field propagation becoming dominant. In this paper, we examine a reconfigurable intelligent surface (RIS)-assisted XL-MIMO communication system to serve two distinct groups of users, namely near-field users (NFUEs), directly served by the XL-MIMO, and far-field users (FFUEs), served with the assistance of a RIS. We derive the signal-to-interference-plus-noise ratio (SINR) expressions for whole-array-based precoders, including maximum-ratio transmission (MRT), and zero-forcing (ZF). Moreover, we take into account the spatially, non-stationary channel characteristics, indicating that user terminals may only have visibility of a specific portion of the array, referred to as the visibility region (VR). To further leverage the VR for complexity reduction, we propose a heuristic algorithm designed to determine the VR, while also guaranteeing the individual SINR requirements for both NFUEs and FFUEs. Simulation results indicate that utilizing VR with our proposed heuristic algorithm yields performance comparable to a benchmark utilizing the whole-array, albeit with a notable reduction in the number of antennas and computational complexity.

This paper investigates the integration of beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) into cell-free massive multiple-input multiple-output (CF-mMIMO) systems, focusing on applications involving simultaneous wireless information and power transfer (SWIPT). The system supports concurrently two user groups: information users (IUs) and energy users (EUs). A BD-RIS is employed to enhance the wireless power transfer (WPT) directed towards the EUs. To comprehensively evaluate the system's performance, we present an analytical framework for the spectral efficiency (SE) of IUs and the average harvested energy (HE) of EUs in the presence of spatial correlation among the BD-RIS elements and for a non-linear energy harvesting circuit. Our findings offer important insights into the transformative potential of BD- RIS, setting the stage for the development of more efficient and effective SWIPT networks. Finally, incorporating a heuristic scattering matrix design at the BD-RIS results in a substantial improvement compared to the scenario with random scattering matrix design.

We explore the potential of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to enhance the performance of wireless surveillance systems. The STAR-RIS is deployed between a full-duplex (FD) multi-antenna legitimate eavesdropper (E) and a suspicious communication pair. It reflects the suspicious signal towards the suspicious receiver (SR), while simultaneously transmitting the same signal to E for interception purposes. Additionally, it enables the forwarding of a jamming signal from E to SR, which is located on the back side of the STAR-RIS. To enhance the eavesdropping non-outage probability, we formulate a non-convex joint optimization problem to design the beamforming vectors at E and reflection/transmission phase shift matrices at the STAR-RIS. We adopt the block coordinate descent (BCD) algorithm and propose an approach, mainly based on semi-definite relaxation (SDR) and successive convex approximation (SCA), for solving the resulting decoupled sub-problems. Finally, we compare the performance of the proposed design against low-complexity zero-forcing (ZF)-based beamforming designs.

Super-directive antenna arrays have faced challenges in achieving high realized gains ever since their introduction in the academic literature. The primary challenges are high impedance mismatches and resistive losses, which become increasingly more dominant as the number of elements increases. Consequently, a critical limitation arises in determining the maximum number of elements that should be utilized to achieve super-directivity, particularly within dense array configurations. This paper addresses precisely this issue through an optimization study to design a super-directive antenna array with a maximum number of elements. An iterative approach is employed to increase the array of elements while sustaining a satisfactory realized gain using the differential evolution (DE) algorithm. Thus, it is observed that super-directivity can be obtained in an array with a maximum of five elements. Our results indicate that the obtained unit array has a 67.20% higher realized gain than a uniform linear array with conventional excitation. For these reasons, these results make the proposed architecture a strong candidate for applications that require densely packed arrays, particularly in the context of massive multiple-input multiple-output (MIMO).

An orthogonal time sequency multiplexing (OTSM) scheme using practical signaling functions is proposed under strong phase noise (PHN) scenarios. By utilizing the transform relationships between the delay-sequency (DS), time-frequency (TF) and time-domains, we first conceive the DS-domain input-output relationship of our OTSM system, where the conventional zero-padding is discarded to increase the spectral efficiency. Then, the unconditional pairwise error probability is derived, followed by deriving the bit error ratio (BER) upper bound in closed-form. Moreover, we compare the BER performance of our OTSM system based on several practical signaling functions. Our simulation results demonstrate that the upper bound derived accurately predicts the BER performance in the case of moderate to high signal-to-noise ratios (SNRs), while harnessing practical window functions is capable of attaining an attractive out-of-band emission (OOBE) vs. BER trade-off.

This work proposes novel approaches that jointly design user equipment (UE) association and power control (PC) in a downlink user-centric cell-free massive multiple-input multiple-output (CFmMIMO) network, where each UE is only served by a set of access points (APs) for reducing the fronthaul signalling and computational complexity. In order to maximize the sum spectral efficiency (SE) of the UEs, we formulate a mixed-integer nonconvex optimization problem under constraints on the per-AP transmit power, quality-of-service rate requirements, maximum fronthaul signalling load, and maximum number of UEs served by each AP. In order to efficiently solve the formulated problem, we propose two different schemes according to the different sizes of the CFmMIMO systems. For small-scale CFmMIMO systems, we present a successive convex approximation (SCA) method to obtain a stationary solution and also develop a learning-based method (JointCFNet) to reduce the computational complexity. For large-scale CFmMIMO systems, we propose a low-complexity suboptimal algorithm using accelerated projected gradient (APG) techniques. Numerical results show that our JointCFNet can yield similar performance and significantly decrease the run time compared with the SCA algorithm in small-scale systems. The presented APG approach is confirmed to run much faster than the SCA algorithm in large-scale systems while obtaining an SE performance close to that of the SCA approach. Moreover, the median sum SE of the APG method is up to about 2.8 fold higher than that of the heuristic baseline scheme.

A cell-free massive multiple-input-multiple-output (CF-mMIMO) system is considered for enhancing the monitoring performance of wireless surveillance, where a large number of distributed multiantenna aided legitimate monitoring nodes (MNs) proactively monitor multiple distributed untrusted communication links. We consider two types of MNs whose task is to either observe the untrusted transmitters or jam the untrusted receivers. We first analyze the performance of CF-mMIMO surveillance relying on both maximum ratio (MR) and partial zero-forcing (PZF) combining schemes and derive closed-form expressions for the monitoring success probability (MSP) of the MNs. We then propose a joint optimization technique that designs the MN mode assignment, power control, and MN-weighting coefficient control to enhance the MSP based on the long-term statistical channel state information knowledge. This challenging problem is effectively transformed into tractable forms and efficient algorithms are proposed for solving them. Numerical results show that our proposed CF-mMIMO surveillance system considerably improves the monitoring performance with respect to a full-duplex co-located massive multiple-input-multiple-output (MIMO) proactive monitoring system. More particularly, when the untrusted pairs are distributed over a wide area and use the MR combining, the proposed solution provides nearly a thirty-fold improvement in the minimum MSP over the co-located massive MIMO baseline, and forty-fold improvement, when the PZF combining is employed.

This paper considers compensation issues within cell-free massive multiple-input multiple-output (MIMO) communication systems, under the in-phase and quadrature-phase imbalance (IQI). Both access points (APs) and users are equipped with multiple antennas. We conduct an analysis into the impact of IQI and propose an efficient IQI compensation scheme to overcome the effects of IQI. Analytical expressions for the minimum mean-square error (MMSE) estimation and the achievable spectral efficiency (SE) of each user are derived, both with and without IQI compensation. In addition, to characterize the IQI effect in the massive MIMO regime, we analyze the asymptotic performance of cell-free massive MIMO when the number of APs goes to infinity. The results of our analysis demonstrate that, when the number of APs grows large, a cell-free system with perfect I/Q matching will allow the SE to increase without bound. However, if IQI is present, the system performance will saturate even if the number of APs becomes very large. The introduction of a compensation technique at the APs, that requires only an estimation of the IQI coefficients, is successful in removing this performance limit, hence significantly enhancing the system performance.

To accommodate extremely high data rates, provide high reliability, improve coverage, and meet traffic demands in future wireless communication networks, novel technologies have emerged that exploit electromagnetic waves, large multiple-antenna systems, intelligent reflective surfaces, hardware innovations, new network architectures, and higher frequency bands. Considering advances in information theory and devices, fundamental questions arise for system designers on how to develop synergies between theory and practice. Current design and analysis methods are predominantly based on scalar-quantity, far-field, planar-wavefront, monochromatic, and other non-physically consistent assumptions, which can lead to significant mismatches with systems designed based on realistic propagation models.

To meet the unprecedented mobile traffic demands of future wireless networks, a paradigm shift from conventional cellular networks to distributed communication systems is imperative. Cell-free massive multiple-input multiple-output (CF-mMIMO) represents a practical and scalable embodiment of distributed/network MIMO systems. It inherits not only the key benefits of co-located massive MIMO systems but also the macro-diversity gains from distributed systems. This innovative architecture has demonstrated significant potential in enhancing network performance from various perspectives, outperforming co-located mMIMO and conventional small-cell systems. Moreover, CF-mMIMO offers flexibility in integration with emerging wireless technologies such as full-duplex (FD), nonorthogonal transmission schemes, millimeter-wave (mmWave) communications, ultrareliable low-latency communication (URLLC), unmanned aerial vehicle (UAV)-aided communication, and reconfigurable intelligent surfaces (RISs). In this article, we provide an overview of current research efforts on CF-mMIMO systems and their promising future application scenarios. We then elaborate on new requirements for CF-mMIMO networks in the context of these technological breakthroughs. We also present several current open challenges and outline future research directions aimed at fully realizing the potential of CF-mMIMO systems in meeting the evolving demands of future wireless networks.

We present a simulation framework for evaluating the performance of cooperative reconfigurable intelligent surface (RIS) based systems, which may ultimately deploy an arbitrary number of RISs to overcome adverse propagation-related effects, such as cascaded fading. The physical model underlying the proposed framework considers the (optional) presence of a dominant signal path between the source and RIS, and then between each subsequent stage of the communication link to the destination. Accompanying the dominant signal component is a non-isotropic scattered signal contribution, which accounts for angular selectivity within the cascaded RIS stages between the source and destination. The simulation of the time-correlated scattered signal, reflected by the illuminated reflective elements, is achieved using autoregressive modelling. As a by-product of our analysis, significant insights are drawn which enable us to characterize the amplitude and phase properties of the received signal, and the associated complex autocorrelation functions (ACFs) for the product of multiple Rician channels. For both single and cooperative RIS systems, the outage probability (OP), and important second-order statistics, such as the level crossing rate (LCR) and average outage duration (AOD), are analyzed for a variety of system configurations, accounting for practical limitations, such as phase errors. It is shown that by using multiple RISs cooperatively, the AOD is reduced at a lower signal-to-noise-ratio (SNR) compared to single RIS-assisted transmission under the same operating conditions. Lastly, increased channel variations (i.e., higher maximum Doppler frequencies) are shown to decrease the AOD in the case of absent phase errors; yet, this improvement is not observed when phase errors are present.

We develop a new integrated communications and security (ICAS) design paradigm by leveraging the concept of reconfigurable intelligent surfaces (RISs). In particular, we propose RIS-assisted simultaneous transmission and secret key generation by sharing the RIS for these two tasks. Specifically, the legitimate transceivers intend to jointly optimize the data transmission rate and the key generation rate by configuring the phase-shift of the RIS in the presence of a smart attacker. We first derive the key generation rate of the RIS-assisted physical layer key generation (PLKG). Then, to obtain the optimal RIS configuration, we formulate the problem as a secure transmission (ST) game and prove the existence of the Nash equilibrium (NE), and then derive the NE point of the static game. For the dynamic ST game, we model the problem as a finite Markov decision process and propose a model-free reinforcement learning approach to obtain the NE point. Particularly, considering that the legitimate transceivers cannot obtain the channel state information (CSI) of the attacker in real-world conditions, we develop a deep recurrent Q-network (DRQN) based dynamic ST strategy to learn the optimal RIS configuration. The details of the algorithm are provided, and then, the system complexity is analyzed. Our simulation results show that the proposed DRQN based dynamic ST strategy has a better performance than the benchmarks even with a partial observation information, and achieves “one time pad” communication by allocating a suitable weight factor for data transmission and PLKG.

We study secure communications in cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multi-antenna access points (APs) and protective partial zero-forcing (PPZF) precoding. In particular, we consider an active eavesdropping attack, where an eavesdropper contaminates the uplink channel estimation phase by sending an identical pilot sequence with a legitimate user of interest. We formulate an optimization problem for maximizing the received signal-to-noise ratio (SINR) at the legitimate user, subject to a maximum allowable SINR at the eavesdropper and maximum transmit power at each AP, while guaranteeing specific SINR requirements on other legitimate users. The optimization problem is solved using a path-following algorithm. We also propose a large-scale-based greedy AP selection scheme to improve the secrecy spectral efficiency (SSE). Finally, we propose a simple method for identifying the presence of an eavesdropper within the system. Our findings show that PPZF can substantially outperform the conventional maximum-ratio transmission (MRT) scheme by providing around 2-fold improvement in the SSE compared to the MRT scheme. More importantly, for PPZF precoding scheme, our proposed AP selection can achieve a remarkable SSE gain of up to 220%, while our power optimization approach can provide an additional gain of up to 55% compared with a CF-mMIMO system with equal power allocation.

As one of the sensing tasks for integrated sensing and communications (ISAC), location distinction based specific emitter identification (SEI) plays an important role in location based services. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted SEI system, in which the legitimate emitter installs an RIS to customize the wireless link signature by controlling the ON-OFF state of RIS. Specifically, we consider the worst-case that the legitimate and a suspicious emitter are in the same spatial location. The received signal strength (RSS) of the specific emitter is adopted to analyze the feasibility of the proposed system. Then, we derive the statistical properties of this wireless link signature, and find the interesting insights about the phase-shift matrix configuration and the signal-to-noise-rate (SNR) gain, which showcase the huge potential of the proposed system on the integrated communications and security (ICAS) design in the near future. Afterwards, we derive the optimal detection threshold in the context of the presented metrics. Next, considering the acquisition difficulty of the RSS samples of the suspicious emitter, we use a one-class support vector machine (OC-SVM) to identify the specific emitter. Finally, the actual feasibility of the proposed system is verified via proof-of-concept experiments. The experiment results show that there are 76% and 99% performance improvements for the test statistic based and the OC-SVM based RIS-assisted SEI, respectively.

Arrays with a large number of antennas can achieve outstanding performance for both communication and radar sensing. This motivates us to investigate the transmit beamforming design for massive multiple-input multiple-output integrated sensing and communications (mMIMO-ISAC) systems. Inspired by the facts that linear precoding methods can provide very good performance in mMIMO and that a dual-function ISAC transmit beamformer should be suitable for both communication and radar sensing, we propose an implementation-friendly mMIMO-ISAC transmit beamformer as a weighted combination of a linear precoder and a pre-designed array beamformer. The weights are selected and adjusted through the powers allocated to the beamformers, while the design problem is formulated as a total transmit power minimization, while satisfying the requirements for both sensing and communication. Leveraging the use-and-then-forget strategy, simplified performance metrics for communication and sensing under the proposed mMIMO-ISAC transmit model are derived, which yields linear programming (LP) problems for the design. It is shown that analytical solutions can be obtained and, hence, the proposed design is computationally very attractive. Simulations are carried out to demonstrate the effectiveness and performance of the proposed methods.

Integrated sensing and communication (ISAC) is emerging as a key enabler to address the increasing demands of spectrum and throughput for ubiquitous sensing and communication. Hereafter, we consider the channel estimation and localization for multi-user ISAC systems assisted by the reconfigurable intelligent surface (RIS) technology. In order to acquire precise environmental information, we propose a novel circular RIS architecture with circularly arranged reflecting unit cells. By modeling the training signal as a low-rank third-order canonical polyadic tensor, we transform the channel estimation problem into a tensor deconstruction task. By leveraging the phase mode excitation principle, we develop a customized RIS training pattern, and retrieve the equivalent channel parameters by subspace estimation algorithms. By exploring the characteristics of RIS array manifolds and free-space propagation, we implement a unique decoupling of channel parameters for user localization, which cannot be supported by traditional linear RIS topologies. Moreover, the design degrees of freedom in the spatial and frequency dimensions are also exploited to further enhance the proposed algorithms. Simulation results indicate that the circular RIS-enabled channel estimation schemes can recover the propagation information with remarkable accuracy, thereby offering a high-level resolution of localization.

In this paper, an efficient near-field channel reconstruction and user equipment (UE) localization scheme is proposed for extremely large antenna array (ELAA) systems using a subarray hybrid precoding architecture. Considering the non-negligible signal amplitude and phase variations across the different receive antennas, a more realistic channel model is adopted. The channel environment, with an approximate smooth ground surface, is modeled. In fact, the channel can be divided into a line-of-sight (LoS) path, a reflection path and some non-LoS (NLoS) paths. Based on the sparsity of the channel in the spatial domain, the damped Newtonized orthogonal matching pursuit (DNOMP) algorithm is also proposed to accurately estimate the multipaths, and reconstruct the channel. Then, a UE localization algorithm is proposed, which can detect the existence of the LoS path and locate the UE. A joint localization algorithm is also devised to further increase the positioning reliability. Simulation results verify that the DNOMP algorithm can reconstruct the channel with better NMSE performance than other schemes. The localization algorithm can locate the UE with low error whenever the LoS path exists or not, with an accuracy close enough to the position error bound (PEB), while the joint localization algorithm can further enhance the positioning reliability.

A reconfigurable intelligent surface (RIS) with active elements is an augmented version of an RIS. By equipping all or part of RIS elements with signal processing capabilities, the channel estimation and the design of RIS phases can be further extended, yielding an improvement in the spectral efficiency (SE). In this paper, we first present a novel sensing RIS structure which is efficient for hardware implementation. Unlike partial active elements in previous structures, all elements are available to the RF chains via switches, which enables the traditional channel estimation methods and channel extrapolation to be implemented. Moreover, we make a comprehensive analysis and comparison with other RIS structures from the perspective of channel state information (CSI) acquisition. Considering the large-scale of RIS and base station (BS) array, we model the channel between the user and the RIS, the RIS and the BS using a near-field channel model. Based on the structured channel model, we propose a low-overhead channel reconstruction protocol through a parameter-extracting method, while the training overhead and complexity are also analyzed. In addition, we investigate the RIS elements’ activation strategy to further reduce the training overhead. Finally, numerical results demonstrate that the proposed scheme achieves accurate channel estimation with low overhead, which can also enhance the achievable SE.

In this paper, we consider a downlink (DL) massive multiple-input multiple-output (MIMO) system, where different users have different mobility profiles. To support this system, we propose to use a hybrid orthogonal time frequency space (OTFS)/orthogonal frequency division multiplexing (OFDM) modulation scheme, where OTFS is applied for high-mobility users and OFDM is used for low-mobility users. Two precoding designs, namely full zero-forcing (FZF) precoding and partial zero-forcing (PZF) precoding, are considered and analyzed in terms of per-user spectral efficiency (SE). With FZF, interference among users is totally eliminated at the cost of high computational complexity, while PZF can be used to provide a trade-off between complexity and performance. To apply PZF precoding, users are grouped into two disjoint groups according to their mobility profile or channel gain. Then, zero-forcing (ZF) is utilized for high-mobility or strong channel gain users to completely cancel the inter-group interference, while maximum ratio transmission (MRT) is applied for low-mobility users or users with weak channel gain. To shed light on the system performance, the SE for high-mobility and low-mobility users with a minimum-mean-square-error (MMSE)-successive interference cancellation (SIC) detector is investigated. Our numerical results reveal that the PZF precoding with channel gain grouping can guarantee a similar quality of service for all users. In addition, with mobility-based grouping, the hybrid OTFS/OFDM modulation outperforms the conventional OFDM modulation for high-mobility users.

In this paper, we consider the problem of transmit beamforming design for massive multiple-input multiple-output integrated sensing and communications (MMIMO-ISAC) systems. Different from the existing designs for regular MIMO-ISAC systems, which directly optimize the dual-function beamformer matrix and are computationally expensive especially for large-scale antenna arrays, we propose to construct the beamformer as a weighted sum of the maximum ratio precoder and a desired sensing beamformer. This is motivated by the fact that maximum ratio precoding schemes are simple, and performs well in MMIMO systems. The weights involved in the beamformer are adjusted via optimizing the powers allocated to the users and sensing. A linear programming (LP) problem, which minimizes the total transmit power subject to the performance constraints of communication and sensing, is thus formulated. It is shown that the LP problem can be analytically solved. Therefore, the proposed transmit beamforming design has very low computational complexity. Its effectiveness and performance are illustrated by simulations.

Wireless surveillance, in which untrusted communications links are proactively monitored by legitimate agencies, has started to garner a lot of interest for enhancing the national security. In this paper, we propose a new cell-free massive multiple-input multiple-output (CF-mMIMO) wireless surveillance system, where a large number of distributed multi-antenna aided legitimate monitoring nodes (MNs) embark on either observing or jamming untrusted communication links. To facilitate concurrent observing and jamming, a subset of the MNs is selected for monitoring the untrusted transmitters (UTs), while the remaining MNs are selected for jamming the untrusted receivers (URs). We analyze the performance of CF-mMIMO wireless surveillance and derive a closed-form expression for the monitoring success probability of MNs. We then propose a greedy algorithm for the observing vs, jamming mode assignment of MNs, followed by the conception of a jamming transmit power allocation algorithm for maximizing the minimum monitoring success probability concerning all the UT and UR pairs based on the associated long-term channel state information knowledge. In conclusion, our proposed CF-mMIMO system is capable of significantly improving the performance of the MNs compared to that of the state-of-the-art baseline. In scenarios of a mediocre number of MNs, our proposed scheme provides an 11-fold improvement in the minimum monitoring success probability compared to its colocated mMIMO benchmarker.

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems incorporating simultane-ous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. To optimize both the spectral efficiency (SE) of IUs and harvested energy (HE) of EUs, we propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. We investigate the problem of maximizing the total HE for EUs, considering constraints on SE for individual IUs and minimum HE for individual EUs. Our numerical results showcase that the proposed AP operation mode selection algorithm can provide up to 76% and 130% performance gains over random AP operation mode selection with and without power control, respectively,

We propose a novel two-phase separate channel estimation scheme in reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Based on the sparsity of the channel, the parameters in the user equipment (UE)-RIS channel and RIS-base station (BS) channel can be estimated in two phases and then utilized for channel reconstruction. Different from the cascaded estimation, the proposed method can achieve separate channel estimation and, thus, has higher practicability and creates room for more ingenious transceiver design. Moreover, a new pilot protocol for the RIS phase shift matrix configuration is proposed, such that new users will need only limited pilot resources. Through simulations, we prove that the proposed scheme can achieve precise channel reconstruction with low pilot overhead.

We consider a reflecting intelligent surface (RIS)-assisted massive multiple-input multiple-output (MIMO) system to facilitate simultaneous wireless information and power transfer (SWIPT) towards two groups of information users (IUs) and energy users (EUs) over Rician fading channels. By considering partial zero-forcing (PZF) precoding at the base station (BS), we derive closed-from expressions for the achievable downlink spectral efficiency (SE) of the IUs and average harvested energy at the EUs. Our results rigorously demonstrate the impact of various system parameters on the actual performance. We next propose a maxmin fairness transmit power allocation which seeks to maximize the minimum harvested power by EUs, subject to quality-of-service constraints at all IUs, relying on statistical channel state information (CSI) and a realistic non-linear energy harvesting (EH) model for the EUs. Our numerical results reveal that the interplay between the RIS and massive MIMO can significantly boost the performance of SWIPT in wireless networks.

We consider a cell-free massive MIMO (CF-mMIMO) system with multi-antenna access points (APs) and distributed protective partial zero-forcing (PPZF) precoding, which is prone to an active eavesdropping attack during uplink training. We develop a tractable analytical framework to derive a novel closed-form expression for the spectral efficiency (SE) at the users and eavesdropper (Eve), and, hence, the secrecy SE. These closed-form expressions are of particular importance for enabling further system design. Our findings show that PPZF can substantially outperform the conventional maximum-ratio transmission (MRT) scheme especially when the ratio of number of AP antennas to the number of users is high. Moreover, the secrecy enhancement obtained by using a higher number of AP antennas is more pronounced when Eve is located farther away from the legitimate user. Finally, simulation results validate the accuracy of the derived theoretical analysis.

This contribution proposes a simulation framework for quantifying the performance of employed reconfigurable intelligent surface (RIS) based systems to overcome adverse propagation-related effects. The physical model underlying the proposed framework considers the presence of a dominant signal path between the source and RIS, and then between RIS and the destination. The simulation of the time-correlated scattered signal reflected by the illuminated reflective elements is achieved using autoregressive (AR) modeling. As a by-product of our analysis, significant insights are developed which allow for the characterization of the amplitude and phase properties of the received signal, and the associated complex autocorrelation function (ACF) for the product of two Rician channels. Capitalizing on this, we derive the corresponding first and second order statistics, which lead to the development of useful theoretical and practical insights.

Reconfigurable intelligent surfaces (RISs) play an important role in extending the connectivity and improving the data rate of future wireless communication systems. However, the conventional (passive) RISs have their limitations and, thus, a hybrid-relay RIS (HR-RIS) architecture is proposed to reap the benefits of relaying systems with high power consumption but higher throughput, and passive RIS systems with cascaded fading effects but low complexity. In this paper, we investigate the performance of HR-RISs in a massive multiple-input multiple-output (M-MIMO) system with zero-forcing (ZF) processing, where channel state information (CSI) is unavailable. We first model the uplink/downlink channels and derive the linear minimum mean square error (LMMSE) estimate of the effective channels. We, then, derive a closed-form expression for the signal to interference and noise ratio (SINR) and spectral efficiency (SE). Finally, we provide some useful engineering insights with our asymptotic analysis and numerical results.

Extra large-scale multiple-input multiple-output (MIMO) has been recognized as one of the potential development directions of massive MIMO. By employing even more antennas than massive MIMO in the fifth-generation era, extra large-scale MIMO can further exploit the spatial domain resources and enable ultra high data rates, low latency communications as well as emerging applications, such as sensing and localization, in sixth-generation mobile communication systems. However, with the increase of the size of the antenna array, and the decrease of the distance between a user and the array, new channel properties, that did not manifest in conventional massive MIMO, start to kick in. Most importantly, existing research strategies pertaining to massive MIMO cannot be directly applied or simply extended to fit the extra large-scale MIMO case. Moreover, increasing the number of antennas will inevitably boost the total cost, which refers to not only the high hardware cost, but also the burden of vast processing and computations as well as the substantial training overhead. In this paper, we make a survey on the state-of-the-art on the new channel properties of and low-cost designs for extra large-scale MIMO systems. Particularly, we pursue a mathematical analysis to explain why the new features appear and illustrate how they affect the system model. Furthermore, we summarize and compare the low-cost designs from various perspectives and give our suggestions from a practical deployment point of view.

We consider network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) systems, where full-duplex (FD) transmission is virtually realized via half-duplex (HD) hardware devices. The HD access points (APs) operating in uplink (UL) mode and those operating in downlink (DL) mode simultaneously serve DL and UL user equipments (UEs) in the same frequency bands. We comprehensively analyze the performance of NAFD CF-mMIMO from both a spectral efficiency (SE) and energy efficiency (EE) perspectives. Specifically, we propose a joint optimization approach that designs the AP mode assignment, power control, and large-scale fading (LSFD) weights to improve the sum SE and EE of NAFD CF-mMIMO systems. We formulate two mixed-integer nonconvex optimization problems of maximizing the sum SE and EE, under realistic power consumption models, and the constraints on minimum individual SE requirements, maximum transmit power at each DL AP and UL UE. The challenging formulated problems are transformed into tractable forms and two novel algorithms are proposed to solve them using successive convex approximation techniques. More importantly, our approach can be applied to jointly optimize power control and LSFD weights for maximizing the sum SE and EE of HD and FD CF-mMIMO systems, which, to date, has not been studied. Numerical results show that: (a) our joint optimization approach significantly outperforms the heuristic approaches in terms of both sum SE and EE; (b) in CF-mMIMO systems, the NAFD scheme can provide approximately 30% SE gains, while achieving a remarkable EE gain of up to 200% compared with the HD and FD schemes.

Terahertz (THz) communication is widely deemed the next frontier of wireless networks owing to the abundant spectrum resources in the THz band. Whilst THz signals suffer from severe propagation losses, a massive antenna array can be deployed at the base station (BS) to mitigate those losses through beamforming. Nevertheless, a very large number of antennas increases the BS’s hardware complexity and power consumption, and hence it can lead to poor energy efficiency (EE). To surmount this fundamental problem, we propose a novel array design based on superdirectivity and nonuniform inter-element spacing. Specifically, we exploit the mutual coupling between closely spaced elements to form superdirective pairs. A unique property of them is that all require the same excitation amplitude, and thus can be driven by a single radio frequency chain akin to conventional phased arrays. Moreover, they facilitate multi-port impedance matching, which ensures maximum power transfer for any beamforming angle. After addressing the implementation issues of superdirectivity, we show that the number of BS antennas can be effectively reduced without sacrificing the achievable rate. Simulation results demonstrate that our design offers huge EE gains compared to uncoupled arrays with uniform spacing, and hence could be a radical solution for future THz systems.

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

Driven by the stringent demands of future ultra reliable and low latency communication (URLLC), we provide a comprehensive study for a coherent and non-coherent multiuser multiple-input multiple-output (MU-MIMO) uplink system in the finite blocklength regime. The independent and identically distributed (i.i.d.) Gaussian codebook is assumed for each user. To be more specific, the base station (BS) first uses two popular linear processing schemes to combine the signals transmitted from all users, namely maximum-ratio combining (MRC) and zero-forcing (ZF). Following it, the matched maximum-likelihood (ML) and mismatched nearest-neighbour (NN) decoding metric for the coherent and non-coherent cases are respectively employed at the BS. Under these conditions, the refined third-order achievable coding rate, expressed as a function of the blocklength, average error probability, and the third-order term of the information density (called as the channel perturbation), is derived. With this result in hand, a detailed performance analysis is then pursued, through which, we derive the asymptotic results of the channel perturbation, achievable coding rate, channel capacity, and the channel dispersion. These theoretical results enable us to obtain a number of interesting insights related to the impact of the finite blocklength: i) in our system setting, massive MIMO helps to reduce the channel perturbation of the achievable coding rate, which can even be discarded without affecting the performance with just a small-to-moderate number of BS antennas and number of blocks; ii) under the non-coherent case, even with massive MIMO, the channel estimation errors cannot be eliminated unless the transmit powers in both the channel estimation and data transmission phases for each user are made inversely proportional to the square root of the number of BS antennas; iii) in the non-coherent case and for fixed total blocklength, the scenarios with longer coherence intervals and smaller number of blocks will offer higher achievable coding rate.

Dense arrays can facilitate the integration of multiple antennas into finite volumes. In addition to the compact size, sub-wavelength spacing enables superdirectivity for endfire operation, a phenomenon that has been mainly studied for isotropic and infinitesimal radiators. In this work, we focus on linear dipoles of arbitrary yet finite length. Specifically, we first introduce an array model that accounts for the sinusoidal current distribution (SCD) on very thin dipoles. Based on the SCD, the loss resistance of each dipole antenna is precisely determined. Capitalizing on the derived model, we next investigate the maximum achievable rate under a fixed power constraint. The optimal design entails conjugate power matching along with maximizing the array gain. Our theoretical analysis is corroborated by the method of moments under the thin-wire approximation, as well as by full-wave simulations. Numerical results showcase that a super-gain is attainable with high radiation efficiency when the dipole antennas are not too short and thin.

We provide a performance evaluation of orthogonal time frequency space (OTFS) modulation in cell-free massive MIMO (multiple-input multiple-output) systems. By leveraging the inherent sparsity of the delay-Doppler (DD) representation of time-varying channels, we apply the embedded pilot-aided channel estimation method with reduced guard intervals and derive the minimum mean-square error estimate of the channel gains from received uplink pilots at the access points (APs). Each AP applies conjugate beamforming to transmit data to the users. We derive a closed-form expression for the individual user downlink throughput as a function of the numbers of APs, users and DD channel estimate parameters. We compare the OTFS performance with that of orthogonal frequency division multiplexing (OFDM) at high-mobility conditions. Our findings reveal that with uncorrelated shadowing, cell-free massive MIMO with OTFS modulation achieves up to 35% gain in 95%-likely per-user throughput, compared with the OFDM counterpart. Finally, the increase in the per user throughput is more pronounced at the median rates over the correlated shadowing scenarios.

This paper considers orthogonal time frequency space (OTFS)-based uplink multiuser multiple-input multiple-output (MU-MIMO) systems in the ultra-reliable-and-low-latency communication (URLLC) space. The maximum-ratio-combining (MRC) scheme is first applied at the base station (BS) to combine all the received symbols and then the maximum likelihood (ML) decoding metric is employed to decode the intended symbols. To better characterize the system performance, we consider the ideal pulse shape functions at both users and BS side while the effect of fractional delay and Doppler shift (FDDS) is also taken into account. Based on this, we first derive the theoretical refined achievable coding rate under short-packet transmission for both joint and individual decoding at the BS. Subsequently, the corresponding asymptotic results under massive MIMO are presented. Capitalizing on these results, we draw a series of interesting insights: i) when the sampling resolution along the delay domain is not high enough, the achievable coding rate is more accurate than the channel capacity as the performance metric; ii) almost no diversity gain is obtained for the FDDS under the case considered in this paper; iii) the achievable coding rate is agnostic to the users' speed.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for the next generation networks. By utilizing tools from stochastic geometry, we develop a meta distributed-based analytical framework to study the effect of the large-scale deployment of the RIS on the performance of a millimeter wave (mmWave) cellular network. Specifically, the locations of the base stations (BSs) are modeled as Poisson point processes (PPPs). In addition, the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. By considering the randomness of the locations and orientations of the RISs and the particular characteristics of mmWave communications, we provide a statistical characterization of the path loss for the BSs and RISs and derive the analytical expressions for the k-th moment of the conditional success probability, the area spectral efficiency and the energy efficiency. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs.

This paper considers uplink multiple-user multiple-input multiple-output (MU-MIMO) systems, in which multiple single-antenna users transmit signals to a multiple-antenna base station (BS) simultaneously. The maximum-ratio-combining (MRC) detection scheme is applied at the BS. Our main focus is on the non-asymptotic concentration of measure analysis for the instantaneous rate. Firstly, the tail probability of the instantaneous rate is derived, from which, a trade-off function is proposed and optimized. Our solution determines the coefficient included in the tail probability and reveals a trade-off between the tail probability and the offset of the instantaneous rate from its mean value. Subsequently, based on the tail probability, a narrow interval that the instantaneous rate falls within with high probability is provided. We show that this narrow interval shrinks with the number of BS antennas. Finally, we use our non-asymptotic results to theoretically characterize the outage probability.

We consider the channel estimation and environment mapping problems in multiple-input multiple-output orthogonal frequency division multiplexing systems empowered by intelligent reconfigurable surfaces (IRSs). In order to acquire more in-depth environmental information, as well as, to flexibly take into account existing real-life infrastructure, we propose a novel three-dimensional conformal IRS architecture consisting of reflective unit cells distributed on curved surfaces. We model the training signal as a third-order canonical polyadic tensor and construct a tensor factorization problem. Given specific conditions on the allocated temporal-frequency training resources, we develop four channel estimation approaches, i.e., least squares, direct, wideband direct and wideband subspace methods, by leveraging tensor techniques and nonlinear system solvers. By fully exploiting the characteristics of conformal IRSs, we propose two decoupling modes to precisely recover the multipath parameters without ambiguities, which cannot be supported by the traditional IRS planar topologies. We implement scatterer mapping and user positioning tasks based on precise parameter estimates. Simulation results indicate that the proposed conformal IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of environment mapping.

We provide the first-ever performance evaluation of orthogonal time frequency space (OTFS) modulation in cell-free massive multiple-input multiple-output (MIMO) systems. To investigate the trade-off between performance and overhead, we apply embedded pilot-aided and superimposed pilot-based channel estimation methods. We then derive a closed-form expression for the individual user downlink and uplink spectral efficiencies (SEs) as a function of the numbers of APs, users and delay-Doppler domain channel estimate parameters. Based on these analytical results, we also present new scaling laws that the AP’s and user’s transmit power should satisfy, to sustain a desirable quality of service. It is found that when the number of APs,$M_{a}$, grows without bound, we can reduce the transmit power of each user and AP proportionally to$1/M_{a}$and$1/M_{a}^{2}$, respectively, during the uplink and downlink phases. We compare the OTFS performance with that of orthogonal frequency division multiplexing (OFDM) at high-mobility conditions. Our findings reveal that, OTFS modulation with embedded pilot-based channel estimation provides up to 20-fold gain over the OFDM counterpart in terms of 95%-likely per-user downlink SE. Finally, with superimposed pilot-based channel estimation, the increase in the uplink sum SE is more pronounced when the channel delay spread is increased.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for millimeter wave (mmWave) networks. In this paper, we utilize tools from stochastic geometry to study the performance of a RIS-assisted mmWave cellular network. Specifically, the locations of the base stations (BSs) and the midpoints of the blockage are modeled as two independent Poisson point processes (PPPs), where the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. The particular characteristics of mmWave communications, i.e., directional beamforming and different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) propagation, are incorporated into our analysis. We derive analytical expressions for the success probability and the area spectral efficiency. The success probability under the special case where the blockage parameter is sufficiently small is also derived. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs. In addition, the tradeoff between the BS and RIS densities is investigated and the results show that the RISs can indeed enable the traditional networks to improve the success probability, especially for the cell-edge region, with limited power consumption.

We consider the channel estimation problem and the channel-based wireless applications in multiple-input multiple-output orthogonal frequency division multiplexing systems assisted by intelligent reconfigurable surfaces (IRSs). To obtain the necessary channel parameters, i.e., angles, delays and gains, for environment mapping and user localization, we propose a novel twin-IRS structure consisting of two IRS planes with a relative spatial rotation. We model the training signal from the user equipment to the base station via IRSs as a third-order canonical polyadic tensor with a maximal tensor rank equal to the number of IRS unit cells. We present four designs of IRS training coefficients, i.e., random, structured, grouping and sparse patterns, and analyze the corresponding uniqueness conditions of channel estimation. We extract the cascaded channel parameters by leveraging array signal processing and atomic norm denoising techniques. Based on the characteristics of the twin-IRS structures, we formulate a nonlinear equation system to exactly recover the multipath parameters by two efficient decoupling modes. We realize environment mapping and user localization based on the estimated channel parameters. Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.

In massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) is required by the base station (BS) to achieve high-performance gains. In frequency division duplexing (FDD) systems, the downlink CSI matrix should be sent back to the BS; unfortunately, the computational and overhead cost of this task is inherently high. Recently, deep learning has been increasingly applied in the space of CSI feedback. However, neural networks entail extra memory and computational requirements, which undermines the deployment of CSI feedback neural networks at the user equipment (UE) side. The conventional lightweight methods such as pruning and quantization requires heavy workload of experiments and difficulty of individually designing training methods for each neural network (NN). In this paper, a novel network lightweight method utilizing knowledge distillation as a training method is introduced to lighten the computation burden of the encoder at the UEs. Knowledge distillation (KD) aims at transferring knowledge from a complex network to a simple network and improving the performance of the simple network close to the complex network. Our numerical experiments demonstrate that the performance of the proposed network can be improved with KD.

Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and inter-symbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies over the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multi-antenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.

We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information.

Cell-free massive multiple-input multiple-output (MIMO) system is a promising architecture for next generation wireless systems by deploying a very large number of distributed access points (APs), which simultaneously serve a smaller number of user equipments (UEs) over the same time-frequency resources. It guarantees uniformly good service at high spectral efficiency with simple linear precoding techniques and max-min power control. In this article, we propose a new joint maximum-ratio and zero-forcing (JMRZF) precoding scheme, where part of APs are combined to perform centralized zero-forcing (ZF), while other APs apply simple maximum-ratio transmission (MRT). Our proposed precoder offers an adaptable trade-off between the spectral efficiency and front-haul signalling overhead. A corresponding AP subset selection scheme is also proposed which is based on large-scale fading coefficients. A closed-form expression for the achievable spectral efficiency of our proposed scheme is derived, which represents a generalized result including both fully distributed MRT and fully centralized ZF cases. Based on this closed-form expression, max-min power control is formulated and solved via the second order cone and first order methods. The former can obtain the global optimal solution, but its computational complexity is very high. On the other hand, the latter technique is sub-optimal, yet, it has very low computational complexity. Hence, it is suitable for large-scale cell-free massive MIMO systems with hundreds or thousands of APs and users. Numerical results show that our proposed JMRZF scheme can substantially outperform the local precoding schemes, even when a small part of APs are combined to deploy ZF and is implementable even when each AP has very few antennas. In addition, it is shown that our max-min power controls improves the spectral efficiency significantly, compared to the uniform power control scheme.

In Massive MIMO base stations (BSs), the hardware design needs to balance high spectral efficiency (SE) with low complexity. The level of hardware impairments (HWIs) indicates how strong the signal distortion introduced by hardware imperfections is. In particular, the analog-to-digital converters (ADCs) have an important impact on signal distortion and power consumption. This article addresses the fundamental problem of selecting the optimal hardware quality in the Massive MIMO space. In particular, we examine the optimal HWI and ADC bit allocation per BS antenna to maximize the SE. The results show that in co-located arrays with low channel gain variations across antennas, equal ADC bit allocation is optimal. In contrast, cell-free Massive MIMO systems benefit the most from optimizing the ADC bit allocation achieving improvements in the order of 2 [bit-per-channel-use] per user equipment when using regularized zero-forcing (RZF). In addition, when including the impact of power consumption in cell-free Massive MIMO with RZF, allocating low values of mixed ADC bit resolutions across the BS antennas can increase the energy efficiency up to 30% compared to equal ADC bit allocation.

The$\kappa $-$\mu $/ inverse gamma and$\eta $-$\mu $/ inverse gamma composite fading models are presented and extensively investigated in this paper. We derive closed-form expressions for the fundamental statistics of the$\kappa $-$\mu $/ inverse gamma composite fading model, such as the probability density function (PDF), cumulative distribution function (CDF). Additionally, we solve the associated integral that is commonly used to obtain the moment generating function (MGF) of statistical distributions to provide an MGF-type function which is valid for performance analysis over the specified parameter space. Analytic expressions for the PDF, higher order moments and AF are also derived for the$\eta $-$\mu $/ inverse gamma composite fading model, while infinite series expressions are obtained for the corresponding CDF and MGF-type function. The suitability of the new models for characterizing composite fading channels is demonstrated through a series of extensive field measurements for wearable, cellular, and vehicular communications. For all of the measurements, two propagation geometry problems with special relevance to the two new composite fading models, namely the line-of-sight (LOS) and non-LOS (NLOS) channel conditions, are considered. It is found that both the$\kappa $-$\mu $/ inverse gamma and$\eta $-$\mu $/ inverse gamma composite fading models provide an excellent fit to fading conditions encountered in the field. The goodness-of-fit of these two composite fading models is also evaluated and compared using the resistor-average distance. As a result, it is shown that the$\kappa $-$\mu $/ inverse gamma composite fading model provides a better fit compared to the$\eta $-$\mu $/ inverse gamma composite fading model when strong dominant signal components exist. On the contrary, the$\eta $-$\mu $/ inverse gamma composite fading model outperforms the$\kappa $-$\mu $/ inverse gamma composite fading model when there is no strong dominant signal component and/or the parameter$\eta $is not equal to unity, indicating that the scattered wave power of the in-phase and quadrature components of each cluster of multipath are not identical.

We consider the channel estimation problem in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems assisted by intelligent reconfigurable surfaces (IRSs). To avoid the inherent estimation ambiguities of the two-hop channels from mobile stations (MS) to the base station (BS), we adopt a hybrid IRS architecture composed of passive reflectors and active sensors, and establish two independent subproblems of estimating the MS-to-IRS and BS-to-IRS channels. By leveraging the sparse characteristics of high-frequency propagation, we model the training signals as multi-dimensional canonical polyadic decomposition (CPD) tensors with missing fibers or slices. We develop algebraic algorithms to solve the tensor completion problems and recover channel multipath parameters, i.e., angles of arrival, time delays and path gains. Our methods require neither random initialization nor iterative operations, and for these reasons they can perform robustly with a low computational complexity. Moreover, we investigate the uniqueness condition of CPD tensor completion, which can be utilized to inform both the physical design of hybrid IRSs and the time-frequency resource allocation of training strategies. Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity.

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths and antenna arrays and increased densities of base stations enhance the delay and angular resolution. This study considers the joint location and velocity estimation of user equipment (UE) and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results by using neural networks (NNs) for localization. However, the black box NN localization method has limited robustness and accuracy and relies on a prohibitive amount of training data to increase localization accuracy. Thus, we propose a model-based learning network for localization to address these problems. In comparison with the black box NN, we combine NNs with geometric models. Specifically, we first develop an unbiased weighted least squares (WLS) estimator by utilizing hybrid delay and angular measurements, which determine the location and velocity of the UE in only one estimator, and can obtain the location and velocity of scatterers further. The proposed estimator can achieve the Cramér-Rao lower bound under small measurement noise and outperforms other state-of-the-art methods. Second, we establish a NN-assisted localization method called NN-WLS by replacing the linear approximations in the proposed WLS localization model with NNs to learn the higher-order error components, thereby enhancing the performance of the estimator, especially in a large noise environment. The solution possesses the powerful learning ability of the NN and the robustness of the proposed geometric model. Moreover, the ensemble learning is applied to improve the localization accuracy further. Comprehensive simulations show that the proposed NN-WLS is superior to the benchmark methods in terms of localization accuracy, robustness, and required time resources.

Accurate channel state information (CSI) is essential at the base station (BS) in order to serve multiple users simultaneously on the same time-frequency resources. Explicit low-rate CSI feedback from each user terminal to the BS is indispensable in frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) hybrid precoding systems. In the presence of correlated Rician fading channels with arbitrary rank, and in order to reduce the feedback overhead, a unified adaptive statistical codebook is designed by leveraging the long-term first and second order channel statistics. Moreover, as the feedback overhead scales linearly with the number of BS antennas, which introduces a bottleneck in FDD massive MIMO systems, an adaptive two-stage reduced dimensionality codebook design is developed. The main idea stems from the fact that the high dimensional analog beamforming stage could get away with coarse CSI, e.g., statistical CSI, while the low dimensional baseband precoding stage acquires finite-rate quantized CSI of the reduced dimensionality channel to eliminate multiuser interference. These steps reduce significantly the CSI training and feedback overhead to scale linearly with the number of radio-frequency chains instead. For comparison purposes, the two extremes of full and statistical CSI at the BS are also included. The performance gap between these two extremes justifies the importance of the proposed two-stage reduced dimensionality adaptive statistical codebook.

We study the uplink performance of massive multiple-input multiple-output (MIMO) when users are equipped with multiple antennas. To this end, we consider a generalized channel model that accounts for line-of-sight propagation and spatially correlated multipath fading. Most importantly, we employ the Weichselberger correlation model, which has been shown to alleviate the deficiencies of the popular Kronecker model. The main contribution of this paper is a rigorous closed-form expression for the uplink spectral efficiency using maximum-ratio combining and minimum mean square error channel estimation. Our result is a non-trivial generalization of previous results on massive MIMO with spatially correlated channels, thereby enabling us to have suitable designs for future massive MIMO systems. Numerical simulations corroborate our analysis and provide useful insights on how different propagation conditions affect system performance.

The concept of large intelligent surface (LIS)-based communication has recently attracted increasing research attention, where a LIS is considered as an antenna array whose entire surface area is available for radio signal transmission and reception. In order to provide a fundamental understanding of LIS-based communication, this paper studies the uplink performance of LIS-based communication with matched filtering in the presence of a line-of-sight channel. We first study the new features introduced by LIS. In particular, the array gain, spatial resolution, and the capability of interference suppression are theoretically presented and characterized. Then, we study two possible LIS system layouts, i.e., centralized LIS (C-LIS) and distributed LIS (D-LIS), and propose a user association scheme aiming to maximize the minimum user spectral efficiency (SE). Simulation results compare the achievable SE between two system layouts. We observe that the proposed user association algorithm significantly improves the performance of D-LIS, and with the help of it, the per-user achievable SE in D-LIS outperforms that in C-LIS in most considered scenarios.

Massive connectivity is a key requirement for the Internet of Things (IoT). In practice, the network should be capable of accommodating thousands of devices and meeting their traffic demands. In this article, we consider the access phase for IoT in a mixed-analog-to-digital converter distributed massive multiple-input-multiple-output system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion. For users with light-load traffic demands, by formulating the user activity detection (UAD) and channel estimation (CE) into a compressed sensing problem, we provide a low-complexity algorithm solver which requires no prior information. The simulation results verify that the proposed algorithm can effectively detect user activity and estimate channel state information (CSI) between the users and access points (APs). To satisfy the throughput requirements of heavy-load users, after UAD and CE, a two-step dynamic clustering is proposed for coordinated multipoint transmission using the large-scale fading (LSF) information. The impact of quantization noise on LSF estimation is investigated, as well as, a corresponding compensation method and accuracy bound. By detecting the clustering behavior among users in the first step, the complexity of the joint user and AP clustering is substantially reduced. The numerical results reveal that the proposed algorithm can offer significant performance gains in various scenarios with fast convergence.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

Multiple antenna technologies have attracted much research interest for several decades and have gradually made their way into mainstream communication systems. Two main benefits are adaptive beamforming gains and spatial multiplexing, leading to high data rates per user and per cell, especially when large antenna arrays are adopted. Since multiple antenna technology has become a key component of the fifth-generation (5G) networks, it is time for the research community to look for new multiple antenna technologies to meet the immensely higher data rate, reliability, and traffic demands in the beyond 5G era. Radically new approaches are required to achieve orders-of-magnitude improvements in these metrics. There will be large technical challenges, many of which are yet to be identified. In this paper, we survey three new multiple antenna technologies that can play key roles in beyond 5G networks: cell-free massive MIMO, beamspace massive MIMO, and intelligent reflecting surfaces. For each of these technologies, we present the fundamental motivation, key characteristics, recent technical progresses, and provide our perspectives for future research directions. The paper is not meant to be a survey/tutorial of a mature subject, but rather serve as a catalyst to encourage more research and experiments in these multiple antenna technologies.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

To fully reap the benefits of massive multiple-input multiple-output hybrid analog and digital precoding in frequency division duplexing single-cell systems, a two-stage precoder is developed utilizing the signal-to-leakage-plus-noise ratio metric. The main idea of this technique is to jointly design the analog precoder based only on the long-term channel statistics information at the transmitter, i.e., the channel mean and reconstructed reduced rank covariance statistics, while the digital precoder is designed based on the instantaneous channel state information of the reduced dimensionality effective reconstructed channel. Consequently, we can significantly reduce the downlink training and uplink feedback overhead analogously to the rank of the resultant effective channel. The two extremes of full channel state information at the transmitter (CSIT) and statistical CSIT are also investigated. The performance gap between the full and statistical CSIT corroborates the importance of the proposed two-stage CSIT approach. These precoders are then extended to multi-cell systems. It is shown that the digital baseband precoder design problem reduces to the generalized Rayleigh quotient problem, while the analog precoder design problem reduces to the quotient trace problem, also known as the ratio trace problem. These dimensionality reduction problems are solved via the generalized eigenvalue decomposition method. Finally, in the presence of multiuser diversity where only a subset of the users are scheduled, to considerably alleviate the channel estimation and feedback overhead burden, a low-complexity one-stage and two-stage CSIT joint user scheduler and precoder algorithms are developed.

We consider the channel estimation problem in millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with hybrid analog-digital architectures. Leveraging the spatial- and frequency-wideband (dual-wideband) effects in massive MIMO scenarios, we derive a spatial-frequency channel model with dual-wideband effects that incorporates the multipath parameters, i.e., time delay, complex gain, angle of departure/arrival. We adopt a successive beam training scheme and formulate the training OFDM signal as a third-order low-rank tensor fitting a canonical polyadic (CP) model with factor matrices containing the channel parameters. Exploiting the Vandermonde nature of factor matrices, we propose a structured CP decomposition-based channel estimation strategy aided by the spatial smoothing method, where two dedicated algorithms with particular tensor modeling and parameter recovery operations are developed. The proposed scheme leverages standard linear algebra, and, hence, avoids the random initialization problem and iterative procedure. An analysis of the uniqueness condition of CP decomposition is also pursued. Simulation results indicate that the proposed strategy achieves enhanced estimation performance, which outperforms the traditional approaches in terms of accuracy, robustness and complexity.

Although uplink (UL) downlink (DL) decoupling (DUDe) brings significant gains in the UL throughput of decoupled user equipments (DeUEs) in heterogeneous networks, channel estimation and DL performance of DeUEs are worse than the coupled UEs due to the DUDe property and the cell edge effect. To address these fundamental problems, we propose a transmission scheme with data-aided (DA) minimum mean square error (MMSE) channel estimator and zero-forcing (ZF) interference nulling (IN) precoding for a two-tier multi-cell HetNet with DUDe. We first present a method to estimate the bit error rate (BER) of UL data, then, derive the form of DA MMSE estimator, which utilizes decoded UL data, estimated BER and known UL training sequences to jointly estimate the DL channels of DeUEs. ZF IN precoding uses the estimated channels of DeUEs to cancel the nearest DL interference without any cooperation and message transmission. Also, we derive a tight approximation to the achievable DL rate of DeUEs and analyze the benefits of the DA estimator and ZF IN precoding. Our simulations show that DA MMSE estimator outperforms the conventional MMSE counterpart, while the proposed scheme improves the DL performance of both DeUEs and macro UEs, though the rate gain may be degraded by pilot contamination and inter-cell interference.

In this paper, we study a multi-pair two-way half-duplex decode-and-forward (DF) massive multiple-input multiple-output (MIMO) relaying system, in which multiple single-antenna user pairs can exchange information through a massive MIMO relay. For low-complexity processing, zero-forcing reception/zero-forcing transmission (ZFR/ZFT) is employed at the relay. First, we analytically study the large-scale approximations of the sum spectral efficiency (SE). Furthermore, we focus on three specific power scaling laws to study the trade-off between the transmit powers of each pilot symbol, each user and the relay, and also focus on how the transmit powers scale with the number of relay antennas, M , to maintain a finite SE performance. Additionally, we consider a practical power consumption model to investigate the energy efficiency (EE), and illustrate the impact of M and the interplay between the power scaling laws and the EE performance. Finally, we consider the system fairness via maximizing the minimum achievable SE among all user pairs.

The concept of large intelligent surface (LIS)-based communication has recently raised research attention, in which a LIS is regarded as an antenna array whose entire surface area can be used for radio signal transmission and reception. To provide a fundamental understanding of LIS-based communication, this paper studies the uplink (UL) performance of LIS-based communication with matched filtering. We first investigate the new properties introduced by LIS. In particular, the array gain, spatial resolution, and the capability of interference suppression are theoretically presented and characterized. Then, we study two possible LIS system layouts in terms of UL, i.e., centralized LIS (C-LIS) and distributed LIS (D-LIS). Our analysis showcases that a centralized system has strong capability of interference suppression; in fact, interference can nearly be eliminated if the surface area is sufficient large or the frequency band is sufficient high. For D-LIS, we propose a series of resource allocation algorithms, including user association scheme, orientation control, and power control, to extend the coverage area of a distributed system. Simulation results show that the proposed algorithms significantly improve the system performance, and even more importantly, we observe that D-LIS outperforms C-LIS in microwave bands, while C-LIS is superior to D-LIS in mmWave bands. These observations serve as useful guidelines for practical LIS deployments.

Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokéMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20-80 percent and reduced data transfer and communication frequency between the edge node and the cloud by up to 95 percent. These results highlight the potential of fog computing for improving the quality of service and experience.

Favorable propagation (FP) and channel hardening (CH) are desired properties in massive multiple-input multiple-output (MIMO) systems. To date, these properties have primarily been analyzed for classical statistical channel models, or ray-based models with very specific angular parameters and distributions. This paper presents a thorough mathematical analysis of the asymptotic system behavior for ray-based channels with arbitrary ray distributions, and considers two types of antenna array structures at the cellular base station: a uniform linear array (ULA) and a uniform planar array (UPA). In addition to FP and channel hardening, we analyze the large system potential (LSP) which measures the asymptotic ratio of the expected power in the desired channel to the expected total interference power when both the antenna and user numbers grow. LSP is said to hold when this ratio converges to a positive constant. The results demonstrate that while FP is guaranteed in ray-based channels, CH may or may not occur depending on the nature of the model. Furthermore, we demonstrate that LSP will not normally hold as the expected interference power grows logarithmically for both ULAs and UPAs relative to the power in the desired channel as the system size increases. Nevertheless, we identify some fundamental and attractive properties of massive MIMO in this limiting regime.

We propose a novel deep energy autoencoder (EA) for noncoherent multicarrier multiuser single-input multipleoutput (MU-SIMO) systems under fading channels.In particular, a single-user noncoherent EA-based (NC-EA) system, based on the multicarrier SIMO framework, is first proposed, where both the transmitter and receiver are represented by deep neural networks (DNNs), known as the encoder and decoder of an EA.Unlike existing systems, the decoder of the NC-EA is fed only with the energy combined from all receive antennas, while its encoder outputs a real-valued vector whose elements stand for the subcarrier power levels.Using the NC-EA, we then develop two novel DNN structures for both uplink and downlink NC-EA multiple access (NC-EAMA) schemes, based on the multicarrier MU-SIMO framework.Note that NC-EAMA allows multiple users to share the same sub-carriers, thus enables to achieve higher performance gains than noncoherent orthogonal counterparts.By properly training, the proposed NC-EA and NC-EAMA can efficiently recover the transmitted data without any channel state information estimation.Simulation results clearly show the superiority of our schemes in terms of reliability, flexibility and complexity over baseline schemes.

To support the ever increasing number of devices in massive multiple-input multiple-output (mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation schemes. To circumvent this fundamental constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The presence of the temporal channel correlation is due to the stationarity of the propagation environment across time. The proposed ML-based predictors involve a pattern extraction implemented via a convolutional neural network, and a CSI predictor realized by an autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network. Closed-form expressions for the user uplink and downlink achievable spectral efficiency and average per-user throughput are provided for the ML-based time division duplex schemes. Our numerical results demonstrate that the proposed ML-based predictors can remarkably improve the prediction quality for both low and high mobility scenarios, and offer great performance gains on the per-user achievable throughput.

Favorable propagation (FP) and channel hardening are desired properties in massive multiple-input and multiple-output (MIMO) systems, where nearly optimal performance is achieved with linear processing techniques, such as maximal-ratio combining. To date, these properties have primarily been analyzed for statistical channel models, or ray-based models with very specific angular parameters and distributions. This paper presents a thorough mathematical analysis of the asymptotic system behavior for ray-based channels with arbitrary ray distributions and a uniform linear array at the base station. In addition to FP and channel hardening, we analyze the large system potential (LSP) which measures the asymptotic signal-to-interference ratio when both the antenna and user numbers grow at an equal rate. The results demonstrate that while FP is guaranteed in ray-based channels, channel hardening may or may not occur depending on the nature of the model. Furthermore, we demonstrate that LSP will not normally hold as the interference power grows logarithmically relative to the signal as the array size increases. Nevertheless, we identify some fundamental and attractive properties of massive MIMO in this limiting regime.

The performance of linear multi-user multiple-input multiple-output (MU-MIMO) systems has been extensively studied for classical statistical channel models. In contrast, there is little analysis for ray-based models, which are physically motivated, feature prominently in standards and have been experimentally validated. Thus, we present a novel analytical framework for zero forcing (ZF) and maximal ratio combining (MRC) applicable to such models. Specifically, using a central result for averaging in the angular domain, we derive accurate expressions for ZF signal-to-noise ratio and MRC signal, interference and noise powers. The remarkably simple expressions offer the following insights into the effects of the propagation environment. While ZF is robust to parameters such as cluster and subray angular spreads, MRC interference is highly sensitive to them. We show that the performance scales linearly with the number of antennas, and that it degrades with narrow angular spreads and as the propagation moves toward the antenna end-fire. Finally, by evaluating the variance of the MRC interference, we observe that an approximation to the MRC SINR widely used for classical statistical models, is inaccurate in ray-based channels.

In this paper, we analyze and compare the performance of conventional maximum-ratio (MR) precoding to a proposed alternative scheme, when applied to a massive multiple-input multiple-output (MIMO) system with multiple-antenna users. In the proposed MR precoding scheme, each channel vector is divided by its norm square to increase the degree of hardening of the effective channel gains at the users. We derive closed-form expressions for the achievable spectral efficiency (SE) of both the conventional and proposed precoding schemes. These closed-form expressions are very simple and useful for further system design. For instance, for both precoding schemes, our results justify the motivation for additional user antennas since significant performance improvements are observed. The proposed scheme produces greater performance when compared to the conventional one across a range of system set-ups. More specifically, the proposed scheme is most effective in systems where the numbers of users and user antennas are kept small.

In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signal- to-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.

This paper tackles the problem of channel state information acquisition in mobile frequency- division-duplex large scale antenna systems and proposes a novel low-complexity low overhead method to track time-varying channels. Given the spatial reciprocity between uplink and downlink, the frequency independent parameters are tracked from the uplink, greatly reducing the training and feedback overhead in the downlink. The uplink tracking method consists of two major modules. The first detection module works at the initial time instance to accurately estimate parameters by a comprehensive algorithm. Then, the second tracking module works at the subsequent instances to track the changes by utilizing a low-overhead algorithm as well as the parameters obtained at the previous instance. Especially, a simplified dictionary is further designed to decrease the computational complexity of the tracking module. Numerical results demonstrate that the proposed tracking method can successfully detect the newly occurred and disappeared paths, and accurately trace the changes of the time-varying channel.

5G millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angular resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging problems. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits for the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer- Rao lower bound and attain the desired sub-decimeter level accuracy.

Massive multiple-input multiple-output (MIMO) avails of simple transceiver design which can tackle many drawbacks of relay systems in terms of complicated signal processing, latency, and noise amplification. However, the cost and circuit complexity of having one radio frequency (RF) chain dedicated to each antenna element are prohibitive in practice. In this paper, we address this critical issue in amplify-and-forward (AF) relay systems using a hybrid analog and digital (A/D) transceiver structure. More specifically, leveraging the channel long-term properties, we design the analog beamformer which aims to minimize the channel estimation error and remain invariant over a long timescale. Then, the beamforming is completed by simple digital signal processing, i.e., maximum ratio combining/maximum ratio transmission (MRC/MRT) or zero forcing (ZF) in the baseband domain. We present analytical bounds on the achievable spectral efficiency taking into account the spatial correlation and imperfect channel state information at the relay station. Our analytical results reveal that the hybrid A/D structure with ZF digital processor exploits spatial correlation and offers a higher spectral efficiency compared to the hybrid A/D structure with MRC/MRT scheme. Our numerical results show that the hybrid A/D beamforming design captures nearly 95% of the spectral efficiency of a fully digital AF relaying topology even by removing half of the RF chains. It is also shown that the hybrid A/D structure is robust to coarse quantization, and even with 2-bit resolution, the system can achieve more than 93% of the spectral efficiency offered by the same hybrid A/D topology with infinite resolution phase shifters.

Hybrid transceiver designs for millimeter wave massive multiple-input multiple-output systems are feasible candidates to reduce the volume of radio frequency (RF) chains, decomposing the signal processing into the analog and digital domains. The existing schemes heavily depend on the singular value decomposition to obtain subchannels with uneven power gains, causing bit error rate (BER) performance degradation. In this paper, we propose a hybrid transceiver design based on the uniform channel decomposition (UCD), yielding subchannels with identical gains to improve the BER performance. Inspired by the UCD concept, we derive an equivalent optimization problem and propose two schemes, namely, phase-extraction and iterative update, to determine the RF beamformers, yielding an effective baseband channel with the greatest possible geometric mean of singular values. We apply the UCD with a minimum mean squared error criterion to complete the baseband beamforming. Finally, we combine the hybrid UCD beamforming with the vertical-Bell Labs layered space-time and dirty paper coding, to eliminate the inter-subchannel interference. An asymptotic analysis of the scheme performance is also pursued. The simulation results show that the proposed hybrid scheme outperforms the conventional schemes on the transmission BER, which achieves a spectral efficiency close to that of the fully-digital counterpart.

This paper aims to provide a partial discrete Fourier transform (DFT) pilot sequence assisted joint channel estimation and user activity detection scheme for massive connectivity, in which a large number of devices with sporadic transmission communicate with a base station (BS) in the uplink. The joint channel estimation and device detection problem can be formulated as a compressed sensing single measurement vector or multiple measurement vector (MMV) problem depending on whether the BS is equipped with single or large number of antennas. Due to high hardware cost and power consumption in massive multiple-input multiple-output (MIMO) systems, a mixed analog-to-digital converter (ADC) architecture is considered. In order to accommodate a large number of simultaneously transmitting devices, the joint channel estimation and active user detection are formulated as an MMV problem for the massive connectivity scenario; and the proposed GTurbo-MMV algorithm can precisely estimate the channel state information and detect active devices with relatively low overhead. Furthermore, we study the state evolution (SE) for the MMV problem to obtain achievable bounds on channel estimation and device detection performance, in which both the missing and false detection probabilities can be made tend to zero in the massive MIMO regime. The simulation results confirm the theoretical accuracy of our analysis.

We consider a multi-pair two-way massive multiple-input multiple-out (MIMO) relaying system over Rician fading channels, where multi-pair users exchange their information via the amplify-and-forward (AF) relaying equipped with large number of antenna arrays. Hardware impairments at the relay and imperfect channel state information (CSI) are taken into account. More specifically, we derive a new linear minimum mean- square error (MMSE) channel estimator for the proposed system. It is demonstrated that normalized mean square error (NMSE) is a constant when the pilot power grows to infinity. Moreover, the asymptotic spectral efficiency with maximum ratio processing is presented in closed-form and the power scaling laws are analyzed. Simulation results indicates that massive MIMO is capable of compensating the loss caused by hardware impairments, estimation error and Rician fading.

This paper considers a decode-and-forward (DF) multi-way massive multiple-input multiple-output (MIMO) relay system where many users exchange their data with the aid of a relay station equipped with a massive antenna array. We propose a new transmission protocol which leverages successive cancelation decoding and zero-forcing (ZF) at the users. By using properties of massive MIMO, a tight analytical approximation of the spectral efficiency is derived. We show that our proposed scheme uses only half of the time-slots required in the conventional scheme (in which the number of time-slots is equal to the number of users [1]), to exchange data across different users. As a result, the sum spectral efficiency of our proposed scheme is nearly double the one of the conventional scheme, thereby boosting the performance of multi-way massive MIMO to unprecedented levels. To improve the network energy efficiency, we also propose a power allocation scheme which maximizes the energy efficiency under a given peak power constraint at each user and the relay.

In this paper, we take a fundamental look at the interference characteristics of a lens-based millimeter-wave (mmWave) multiuser multiple-input multiple-output system (MU-MIMO) system. We consider a hybrid architecture, implemented via a bank of radio-frequency (RF) switches which perform beam selection followed by low-complexity uplink maximum-ratio combining (MRC) at baseband. Considering a Rotman lens antenna array in line- of-sight (LoS) propagation, we derive tight analytical approximations to the average (expected) interference power of an arbitrary terminal, with and without the presence of RF switching. The analytical expressions show that without RF switching, the Rotman lens losses its benefits and collapses to a conventional uniform linear array. Our numerical results demonstrate that the expected interference power to a given terminal decreases significantly with RF switching, due to beam selection separating the uplink direction-of-arrivals (DoAs), in contrast to the case without RF switching, which relaxes the beam selection constraint and thus allows very similar DoAs. Overall, the results in this paper emphasize the necessity of RF switching in order to obtain superior performance with lens arrays, over conventional phased arrays.

We consider a massive multiple-input multiple- output system with minimum-mean-squared-error processing on the uplink. A novel analytical framework is proposed to approximate the instantaneous signal-to-interference-plus-noise- ratio (SINR) of an arbitrary user terminal, as well as, the system sum spectral efficiency. Unlike previous studies, our methodology considers spatially correlated Ricean fading, with unequal Ricean K-factors, spatial correlation matrices and link gains across all terminals. Under this fully heterogeneous setting, we demonstrate that the SINR of a terminal can be tightly approximated by a linear combination of non-central chi-squared random variables, where the scaling depends on the individual link gains, K-factors, and eigenvalues of the terminal specific correlation matrices. Our approximations remain tight across the considered spatial correlation models, K-factor models, average uplink signal-to-noise-ratios and number of receive antennas. Leveraging the general form of the SINR and sum spectral efficiency, an analytical method to approximate their statistical moments is presented utilizing the moment generating function. The generality of the aforementioned analytical results is demonstrated via several special cases of practical relevance.

Spatial correlation across an antenna array is known to be detrimental to the terminal signal-to- interference-plus-noise-ratio (SINR) and system spectral efficiency. For a downlink multiuser multiple-input multiple-output system (MU-MIMO), we show that the widely used, yet overly simplified, correlation models which generate fixed correlation patterns for all terminals tend to underestimate the system performance. This is in contrast to more sophisticated, yet physically motivated, remote scattering models that generate variations in the correlation structure across multiple terminals. The remote scattering models are parameterized with measured data from a recent 2.53 GHz urban macrocellular channel measurement campaign in Cologne, Germany. Assuming spatially correlated Ricean fading, with maximum-ratio transmission precoding, tight closed-form approximations to the expected (average) SINR, and ergodic sum spectral efficiency are derived. The expressions provide clear insights into the impact of variable correlation patterns on the above performance metrics. Our results demonstrate the sensitivity of the MU-MIMO performance to different correlation models, and provide a cautionary tale of its impact.

Novel composite fading models were recently proposed based on inverse gamma distributed shadowing conditions. These models were extensively shown to provide remarkable modeling of the simultaneous occurrence of multipath fading and shadowing phenomena in emerging wireless scenarios such as cellular, off-body and vehicle-to-vehicle communications. Furthermore, the algebraic representation of these models is rather tractable, which renders them convenient to handle both analytically and numerically. Based on this, the present contribution analyzes the ergodic capacity over the recently proposed $\kappa-\mu$ / inverse gamma composite fading channels, which were shown to characterize excellently multipath fading and shadowing in line-of-sight communication scenarios, including realistic vehicular communications. Novel analytic expressions are derived which are subsequently used in the analysis of the corresponding system performance. In this context, the offered results are compared with respective results from cases assuming conventional fading conditions, which leads to the development of numerous insights on the effect of the multipath fading and shadowing severity on the achieved capacity levels. It is expected that these results will be useful in the design of timely and demanding wireless technologies such as wearable, cellular and inter-vehicular communications.

The η-μ / inverse gamma and κ-μ / inverse gamma distributions were recently introduced as particularly flexible and tractable composite fading models that provide accurate characterization of multipath and shadowing effects, which are encountered simultaneously during wireless transmission in emerging communication scenarios such as off-body, cellular and vehicular-to-vehicular communications. The present contribution analyzes the symbol error rate performance of digital communications over these fading channels. To this end, we derive novel analytic expressions for the symbol error rate of multiple amplitude based modulated systems under these fading conditions, which are subsequently used in the analysis of the corresponding system performance. In this context, numerous insights are developed on the effect of different fading conditions on the corresponding error rate, which are expected to be useful in the design of timely and demanding wireless technologies such as wearable, cellular and vehicular communication systems.

We consider a multi-way decode-and-forward relaying network with very large antenna arrays at the relay station. In this system, each user and the relay operate in half-duplex and time-division duplexing modes. To exchange information among all users, we propose a new transmission protocol which combines massive multiple-input multiple-output technology with linear processing, self-interference cancelation, and successive cancelation decoding. Our proposed transmission protocol reduces the number of time-slots for data exchange among users by approximately 2 times, compared with the conventional data transmission protocol. For this new topology, we derive a very tight approximation of the spectral efficiency in closed-form assuming perfect channel state information (CSI). Then, a CSI acquisition method at the relay and the users is provided and analyzed. We show via numerical simulations, that the performance gap between imperfect and perfect CSI cases is small. The closed-form expression of the spectral efficiency enables us to design two power allocation schemes. In the first power allocation scheme, we choose the transmit powers at the users and the relay to maximize the sum spectral efficiency, subject to a given quality-of-service requirement for each user. In the second power allocation scheme, the objective is the energy efficiency taking into account the hardware power consumption. Both power allocation schemes can be efficiently executed by iteratively solving a sequence of convex problems. Numerical results verify the effectiveness of the proposed transmission protocol and the power allocation schemes compared with the state of the art.

This paper considers a two-way half-duplex decode-and-forward relaying system, where multiple pairs of single-antenna users exchange information via a multiple-antenna relay. Assuming that the channel knowledge is nonideal and the relay employs maximum ratio processing, we derive a largescale approximation of the sum spectral efficiency (SE) that is tight when the number of relay antennas M becomes very large. Furthermore, we study how the transmit power scales with M to maintain a desired SE. In particular, three special powerscaling cases are discussed and the corresponding asymptotic SE is deduced with clear insights. Our elegant power-scaling laws reveal a tradeoff between the transmit powers of the user/relay and pilot symbol. Finally, we formulate a power allocation problem in terms of maximizing the sum SE and obtain a local optimum by solving a sequence of geometric programming problems.

Closed-form approximations to the expected perterminal signal-to-interference-plus-noise-ratio (SINR) and ergodic sum spectral efficiency of a large multiuser multiple-input multiple-output system are presented. Our analysis assumes correlated Ricean fading with maximum ratio combining on the uplink, where the base station (BS) is equipped with a uniform linear array (ULA) with physical size restrictions. Unlike previous studies, our model caters for the presence of unequal correlation matrices and unequal Rice factors for each terminal. As the number of BS antennas grows without bound, with a finite number of terminals, we derive the limiting expected perterminal SINR and ergodic sum spectral efficiency of the system. Our findings suggest that with restrictions on the size of the ULA, the expected SINR saturates with increasing operating signal-to-noise-ratio (SNR) and BS antennas. Whilst unequal correlation matrices result in higher performance, the presence of strong line-of-sight (LoS) has an opposite effect. Our analysis accommodates changes in system dimensions, SNR, LoS levels, spatial correlation levels and variations in fixed physical spacings of the BS array.

In order to reap the full scale of benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm-frequencies. In this paper, we investigate the steering vector and array factor by considering three types of planar antenna arrays, namely uniform rectangular planar array (URPA), uniform hexagonal planar array (UHPA), and uniform circular planar array (UCPA). Based on these results, we investigate the array directivity/gain and the achievable spectral efficiency in a 3-dimensional massive MIMO system by considering both the azimuth and elevation dimensions. An important observation is that the the maximum array gain, the beamwidth, and the achievable spectral efficiency (SE) for the above three types of planar antenna array configurations are almost identical. The side lobe level and the geometric area of the UHPA configuration are systematically better than those of the UCPA and URPA configurations.

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

For the potential of providing rates of multiple Giga-bps in a single channel, millimeter wave (mmWave) communications have recently attracted substantial research interest. While mmWave technology is already being used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave frequencies in mobile networks, where transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, many significant technical challenges must be tackled. The main objective of this IEEE JSAC Special Issue on “Millimeter wave communications for future mobile networks” is to collect the most recent technical advances in mmWave for future mobile networks. The response from the community to the call has been overwhelming. We received 96 submissions with a call period short than 4 months. Many of the submissions are from the most well known research groups in the field. After a strict review process, we decided to accept 38 papers, which will be published in two issues. The papers were selected based on the technical relevance and merits. Unfortunately, due to space limitations, a number of interesting papers were not selected, despite the merits that they had. We sincerely hope those papers can find other publishing venues.

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation Exprcee and the achievable uplink rate holding for any number of base station antennas M, with the least squares (LS) and minimum mean squared error (MMSE) methods. It is found that RCEE and Exprcee converge to the same constant value when M → ∞, which renders the pilot power allocation (PPA) substantially simplified and a PPA algorithm is proposed to minimize the average Exprcee per user under a total pilot power budget F in multi-cell massive multipleinput multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are significant only with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large F, the performance of the LS approaches to that of the MMSE. Besides, a scheduling strategy is proposed to allocate pilot power in the whole system, which can approach the optimal performance. For the achievable uplink rate, the PPA scheme and improves the minimum achievable uplink rate compared with the EPPA scheme.

Ultra-dense cloud radio access networks (C-RANs) are an example of the architectures that will be critical components of the next-generation wireless systems. In a C-RAN architecture, an amorphous cellular framework, where each user connects to a few nearby remote radio heads (RRHs) to form its own cell, appears to be promising. In this paper, we study the ergodic capacity of such amorphous cellular networks at high signal-to-noise ratios (SNRs) where we model the distribution of the RRHs by a Poisson point process. We derive tractable approximations of the ergodic capacity at high-SNRs for arbitrary antenna configurations, and tight lower bounds for the ergodic capacity when the numbers of antennas are the same at both ends of the link. In contrast to prior works on distributed antenna systems, our results are derived based on random matrix theory and involve only standard functions which can be much more easier evaluated. The impact of the system parameters on the ergodic capacity is investigated. By leveraging our analytical results, we propose two efficient scheduling algorithms for RRH selection for energy-efficient transmission. Our algorithms offer a substantial improvement in energy efficiency compared with the strategy of connecting a fixed number of RRHs to each user.

Due to the difficulty in manipulating the distribution of Wishart random matrices, the performance analysis of multiple-input-multiple-output (MIMO) channels has mainly focused on deriving capacity bounds via Jensen's inequality. However, to the best of our knowledge, the tightness of Jensen's bounds has not yet been rigorously quantified in the general MIMO context. This paper proposes a new methodology for measuring the tightness of Jensen's bounds via the sandwich theorem. In particular, we first compare the tightness of two different pairs of upper/lower bounds for a general class of MIMO channels based on the unordered eigenvalue of the instantaneous correlation matrix and for arbitrary numbers of antennas. The tightness of Jensen's bounds in different channel scenarios is investigated including multiuser MIMO with maximal ratio combining. Our analysis is facilitated by deriving some new results for finite-dimensional Wishart matrices, i.e., for the arbitrary moments of the unordered eigenvalue of central and non-central Wishart matrices. Our results provide very interesting insights into the implications of the system parameters, such as the number of antennas, and signal-to-noise ratio, on the tightness of Jensen's bounds, and showcase the suitability and limitations of Jensen's bounds.

We consider a multi-pair two-way amplify-and-forward relaying system with a massive antenna array at the relay and estimated channel state information, assuming maximum-ratio combining/transmission processing. Closed-form approximations of the sum spectral efficiency are developed and simple analytical power scaling laws are presented, which reveal a fundamental trade-off between the transmit powers of each user/the relay and of each pilot symbol. Finally, the optimal power allocation problem is studied.

Massive multiple-input multiple-output (MIMO) relaying is a promising technological paradigm which can offer high spectral efficiency and substantially improved coverage. Yet, these configurations face some formidable challenges in terms of digital signal processing (DSP) power consumption and circuitry complexity, since the number of radio frequency (RF) chains may scale with the number of antennas at the relay station. In this paper, we advocate that performing a portion of the power-intensive DSP in the analog domain, using simple phase shifters and with a reduced number of RF paths, can address these challenges. In particular, we consider a multipair amplify-and-forward (AF) relay system with maximum ratio combining/transmission (MRC/MRT) and we determine the asymptotic spectral efficiency for this hybrid analog/digital architecture. After that, we extend our analytical results to account for heavily quantized analog phase shifters and show that the performance loss with 2 quantization bits is only 10%.

In this paper, we investigate the spectral efficiency (SE) of massive multiple-input multiple-output (MIMO) systems with a large number of antennas at the base station (BS) accounting for physical space constraints. In contrast to the vast body of related literature, which considers fixed inter-element spacing, we elaborate on a practical topology in which an increase in the number of antennas in a fixed total space induces an inversely proportional decrease in the inter-antenna distance. For this scenario, we derive exact and approximate expressions, as well as simplified upper/lower bounds, for the SE of maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-squared error receivers (MMSE) receivers. In particular, our analysis shows that the MRC receiver is non-optimal for space-constrained massive MIMO topologies. On the other hand, ZF and MMSE receivers can still deliver an increasing SE as the number of BS antennas grows large. Numerical results corroborate our analysis and show the effect of the number of antennas, the number of users, and the total antenna array space on the sum SE performance.

This paper investigates the achievable spectral efficiency (SE) of millimeter wave downlink cellular systems accounting for both small and large-scale fading effects. The base station (BS) employs a uniform linear array (ULA) and maximal ratio transmission. We derive the expectation of the squared inner product for different channel links under the assumption that the users are randomly distributed within a circular-shaped cell. Using this result, a new lower bound on the achievable SE, valid for arbitrary numbers of antennas is derived. The analytical results show that the achievable SE increases with the number of BS antennas, whilst it converges to a saturated value in the high signal-to-noise ratio (SNR) regime and larger inter- antenna spacing.

In this paper, we consider the uplink of a single-cell multi-user single-input multiple-output (MU-SIMO) system with in-phase and quadrature-phase imbalance (IQI). In particular, we investigate the effect of receive (RX) IQI on the performance of MU-SIMO systems with large antenna arrays employing maximum-ratio combining receivers. In order to study how IQI affects channel estimation, we derive a new channel estimator for the IQI-impaired model and show that the higher the value of signal-to-noise ratio, the higher the impact of IQI on the spectral efficiency (SE). Moreover, a novel pilot-based joint estimator of the augmented multiple-input multiple-output (MIMO) channel matrix and IQI coefficients is described, and then, a low-complexity IQI compensation scheme is proposed, which is based on the IQI coefficients' estimation and it is independent of the channel gain. The performance of the proposed compensation scheme is analytically evaluated by deriving a tractable approximation of the ergodic SE assuming transmission over Rayleigh fading channels with large-scale fading. Furthermore, we investigate how many mobile stations should be scheduled in massive MIMO systems with IQI and show that the highest SE loss occurs at the optimal operating point. Finally, by deriving asymptotic power scaling laws and proving that the SE loss due to IQI is asymptotically independent of the number of BS antennas, we show that massive MIMO is resilient to the effect of RX IQI.

This paper presents an analytical performance investigation of both beamforming (BF) and interference cancellation (IC) strategies for a device-to-device (D2D) communication system underlaying a cellular network with an M-antenna base station (BS). We first derive new closed-form expressions for the ergodic achievable rate for BF and IC precoding strategies with quantized channel state information (CSI), as well as, perfect CSI. Then, novel lower and upper bounds are derived which apply for an arbitrary number of antennas and are shown to be sufficiently tight to the Monte-Carlo results. Based on these results, we examine in detail three important special cases including: high signal-to-noise ratio (SNR), weak interference between cellular link and D2D link, and BS equipped with a large number of antennas. We also derive asymptotic expressions for the ergodic achievable rate for these scenarios. Based on these results, we obtain valuable insights into the impact of the system parameters, such as the number of antennas, SNR and the interference for each link. In particular, we show that an irreducible saturation point exists in the high SNR regime, while the ergodic rate under IC strategy is verified to be always better than that under BF strategy. We also reveal that the ergodic achievable rate under perfect CSI scales as log2M, whilst it reaches a ceiling with quantized CSI.

The development of 5G enabling technologies brings new challenges to the design of power amplifiers (PAs). In particular, there is a strong demand for low-cost, nonlinear PAs which, however, introduce nonlinear distortions. On the other hand, contemporary expensive PAs show great power efficiency in their nonlinear region. Inspired by this trade-off between nonlinearity distortions and efficiency, finding an optimal operating point is highly desirable. Hence, it is first necessary to fully understand how and how much the performance of multiple-input multiple-output (MIMO) systems deteriorates with PA nonlinearities. In this paper, we first reduce the ergodic achievable rate (EAR) optimization from a power allocation to a power control problem with only one optimization variable, i.e. total input power. Then, we develop a closed-form expression for the EAR, where this variable is fixed. Since this expression is complicated for further analysis, two simple lower bounds and one upper bound are proposed. These bounds enable us to find the best input power and approach the channel capacity. Finally, our simulation results evaluate the EAR of MIMO channels in the presence of nonlinearities. An important observation is that the MIMO performance can be significantly degraded if we utilize the whole power budget.

In this paper, we consider the uplink of a single-cell massive multiple-input multiple-output (MIMO) system with inphase and quadrature-phase imbalance (IQI). This scenario is of particular importance in massive MIMO systems, where the deployment of lower-cost, lower-quality components is desirable to make massive MIMO a viable technology. Particularly, we investigate the effect of IQI on the performance of massive MIMO employing maximum-ratio combining (MRC) receivers. In order to study how IQI affects channel estimation, we derive a new channel estimator for the IQI-impaired model and show that IQI can substantially downgrade the performance of MRC receivers. Moreover, a low-complexity IQI compensation scheme, suitable for massive MIMO, is proposed which is based on the IQI coefficients' estimation and it is independent of the channel gain. The performance of the proposed compensation scheme is analytically evaluated by deriving a tractable approximation of the ergodic achievable rate and providing the asymptotic power scaling laws assuming transmission over Rayleigh fading channels with log-normal large-scale fading. Finally, we show that massive MIMO effectively suppresses the residual IQI effects, as long as, the compensation scheme is applied.

We investigate the achievable sum rate and energy efficiency of zero-forcing precoded downlink massive multiple-input multiple-output systems in Ricean fading channels. A simple and accurate approximation of the average sum rate is presented, which is valid for a system with arbitrary rank channel means. Based on this expression, the optimal power allocation strategy maximizing the average sum rate is derived. Moreover, considering a general power consumption model, the energy efficiency of the system with rank-1 channel means is characterized. Specifically, the impact of key system parameters, such as the number of users N, the number of BS antennas M, Ricean factor K and the signal-to-noise ratio (SNR) ρ are studied, and closed-form expressions for the optimal ρ and M maximizing the energy efficiency are derived. Our findings show that the optimal power allocation scheme follows the water filling principle, and it can substantially enhance the average sum rate in the presence of strong line-of-sight effect in the low SNR regime. In addition, we demonstrate that the Ricean factor K has significant impact on the optimal values of M, N and ρ.

We study a multiuser multicarrier downlink communication system in which the base station (BS) employs a large number of antennas. By assuming frequency-division duplex operation, we provide a beam domain channel model as the number of BS antennas grows asymptotically large. With this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate before developing necessary conditions to asymptotically maximize the upper bound, with only statistical channel state information at the BS. Inspired by these conditions, we propose a beam division multiple access (BDMA) transmission scheme, where the BS communicates with users via different beams. For BDMA transmission, we design user scheduling to select users within non-overlapping beams, work out an optimal pilot design under a minimum mean square error criterion, and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. The proposed BDMA scheme reduces significantly the pilot overhead, as well as, the processing complexity at transceivers. Simulations demonstrate the high spectral efficiency of BDMA transmission and the advantages in the bit error rate performance of the proposed pilot sequences.

We investigate the achievable ergodic sum-rate of multi-user multiple-input multiple-output systems in Ricean fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by utilizing the Mullen's inequality, which is then used to analyze the effect of channel mean information on the achievable sum-rate. With these results, a novel statistical-eigenmode space-division multipleaccess downlink transmission scheme is proposed. For this scheme, we derive an exact closed-form expression for the achievable ergodic sum-rate. Our results show that the achievable ergodic sum-rate converges to a saturation value in the high signal-to-noise ratio (SNR) region and reaches to a lower limit value in the lower Ricean K-factor range. In addition, we present tractable upper and lower bounds, which are shown to be tight for any SNR and Ricean K-factor value. Finally, the theoretical analysis is validated via numerical simulations.

This paper investigates the achievable sum-rate of uplink massive multiple-input multiple-output (MIMO) systems considering a practical channel impairment, namely, aged channel state information (CSI). Taking into account both maximum ratio combining (MRC) and zero-forcing (ZF) receivers at the base station, we present tight closed-form lower bounds on the sum-rate for both receivers, which provide efficient means to evaluate the sum-rate of the system. More importantly, we characterize the impact of channel aging on the power scaling law. Specifically, we show that the transmit power of each user can be scaled down by 1/√(M), which indicates that aged CSI does not affect the power scaling law; instead, it causes only a reduction on the sum rate by reducing the effective signal-to-interference-and-noise ratio (SINR).

Statistical distributions have been extensively used in modeling fading effects in conventional and modern wireless communications. In the present work, we propose a novel κ - μ composite shadowed fading model, which is based on the valid assumption that the mean signal power follows the inverse gamma distribution instead of the lognormal or commonly used gamma distributions. This distribution has a simple relationship with the gamma distribution, but most importantly, its semi heavy-tailed characteristics constitute it suitable for applications relating to modeling of shadowed fading. Furthermore, the derived probability density function of the κ - μ / inverse gamma composite distribution admits a rather simple algebraic representation that renders it convenient to handle both analytically and numerically. The validity and utility of this fading model are demonstrated by means of modeling the fading effects encountered in body centric communications channels, which have been known to be susceptible to the shadowing effect. To this end, extensive comparisons are provided between theoretical and respective real-time measurement results. It is shown that these comparisons exhibit accurate fitting of the new model for various measurement set ups that correspond to realistic communication scenarios.

In this paper we propose a new composite fading model which assumes that the mean signal power of an η — μ signal envelope follows an inverse gamma distribution. The inverse gamma distribution has a simple relationship with the gamma distribution and can be used to model shadowed fading due to its semi heavy-tailed characteristics. To demonstrate the utility of the new η — μ / inverse gamma composite fading model, we investigate the characteristics of the shadowed fading behavior observed in body centric communications channels which are known to be susceptible to shadowing effects, particularly generated by the human body. It is shown that the η — μ / inverse gamma composite fading model provided an excellent fit to the measurement data. Moreover, using Kullback-Leibler divergence, the η — μ / inverse gamma composite fading model was found to provide a better fit to the measured data than the k — μ / inverse gamma composite fading model, for the communication scenarios considered here.

This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum-rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multicell scenario is also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/√M (where M is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law; instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively.

We study multicarrier multiuser multiple-input multiple-output (MU-MIMO) systems, in which the base station employs an asymptotically large number of antennas. We analyze a fully correlated channel matrix and provide a beam domain channel model, where the channel gains are independent of sub-carriers. For this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate, based on which, we develop asymptotically necessary and sufficient conditions for optimal downlink transmission that require only statistical channel state information at the transmitter. Furthermore, we propose a beam division multiple access (BDMA) transmission scheme that simultaneously serves multiple users via different beams. By selecting users within non-overlapping beams, the MU-MIMO channels can be equivalently decomposed into multiple single-user MIMO channels; this scheme significantly reduces the overhead of channel estimation, as well as, the processing complexity at transceivers. For BDMA transmission, we work out an optimal pilot design criterion to minimize the mean square error (MSE) and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. Simulations demonstrate the near-optimal performance of BDMA transmission and the advantages of the proposed pilot sequences.

Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas, deployed on co-located or distributed arrays. Huge spatial degrees-of-freedom are achieved by coherent processing over these massive arrays, which provide strong signal gains, resilience to imperfect channel knowledge, and low interference. This comes at the price of more infrastructure; the hardware cost and circuit power consumption scale linearly/affinely with the number of BS antennas N. Hence, the key to cost-efficient deployment of large arrays is low-cost antenna branches with low circuit power, in contrast to today's conventional expensive and power-hungry BS antenna branches. Such low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the huge degrees-of-freedom would bring robustness to such imperfections. We prove this claim for a generalized uplink system with multiplicative phase-drifts, additive distortion noise, and noise amplification. Specifically, we derive closed-form expressions for the user rates and a scaling law that shows how fast the hardware imperfections can increase with N while maintaining high rates. The connection between this scaling law and the power consumption of different transceiver circuits is rigorously exemplified. This reveals that one can make √N the circuit power increase as N, instead of linearly, by careful circuit-aware system design.

In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen's inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SE-SDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the impact of the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician $K$-factor, on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician $K$-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zero-forcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician $K$-factor regime.

This paper studies the impact of in-phase and quadrature-phase imbalance (IQI) in two-way amplify-and-forward (AF) relaying systems. In particular, the effective signal-to-interference-plus-noise ratio (SINR) is derived for each source node, considering four different linear detection schemes, namely, uncompensated (Uncomp) scheme, maximal-ratio-combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) based schemes. For each proposed scheme, the outage probability (OP) is investigated over independent, non-identically distributed Nakagami-m fading channels, and exact closed-form expressions are derived for the first three schemes. Based on the closed-form OP expressions, an adaptive detection mode switching scheme is designed for minimizing the OP of both sources. An important observation is that, regardless of the channel conditions and transmit powers, the ZF-based scheme should always be selected if the target SINR is larger than 3 (4.77dB), while the MRC-based scheme should be avoided if the target SINR is larger than 0.38 (-4.20dB).

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas. Such large antenna arrays offer huge spatial degrees-of-freedom for transmission optimization; in particular, great signal gains, resilience to imperfect channel knowledge, and small inter-user interference are all achievable without extensive inter-cell coordination. The key to cost-efficient deployment of large arrays is the use of hardware-constrained base stations with low-cost antenna elements, as compared to today's expensive and power-hungry BSs. Low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the excessive degrees-of-freedom of massive MIMO would bring robustness to such imperfections. We herein prove this claim for an uplink channel with multiplicative phase-drift, additive distortion noise, and noise amplification. Specifically, we derive a closed-form scaling law that shows how fast the imperfections increase with the number of antennas.

We investigate the performance of dual-hop two-way amplify-and-forward (AF) relaying in the presence of inphase and quadrature-phase imbalance (IQI) at the relay node. In particular, the effective signal-to-interference-plus-noise ratio (SINR) at both sources is derived. These SINRs are used to design an instantaneous power allocation scheme, which maximizes the minimum SINR of the two sources under a total transmit power constraint. The solution to this optimization problem is analytically determined and used to evaluate the outage probability (OP) of the considered two-way AF relaying system. Both analytical and numerical results show that IQI can create fundamental performance limits on two-way relaying, which cannot be avoided by simply improving the channel conditions.

The performance of multiuser dual-hop relaying over mixed radio frequency/free-space optical (RF/FSO) links is investigated. RF links are used for the simultaneous data transmission from m single-antenna sources to the relay, which is equipped with n ≥ m receive antennas and a photo-aperture transmitter. The relay operates under the decode-and-forward protocol and utilizes the popular ordered V-BLAST technique to successively decode each user's transmitted stream. A common norm-based ordering approach is adopted, where the streams are decoded in an ascending order. After the V-BLAST decoding, the relay retransmits the initial information to the destination, which is equipped with a photo-detector, via a point-to-point FSO link in m consecutive timeslots. Analytical expressions for the end-to-end outage probability and average symbol error probability of each user are derived. Some engineering insights are manifested, such as the diversity order, the impact of the pointing error displacement on the FSO link and the severity on the turbulence-induced channel fading.

We consider a multipair relay channel, where multiple sources communicate with multiple destinations with the help of a full-duplex (FD) relay station (RS). All sources and destinations have a single antenna, while the RS is equipped with massive arrays. We assume that the RS estimates the channels by using training sequences transmitted from sources and destinations. Then, it uses maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To significantly reduce the loop interference (LI) effect, we propose two massive MIMO processing techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the RS. We derive an exact achievable rate in closed-form and evaluate the system spectral efficiency. We show that, by doubling the number of antennas at the RS, the transmit power of each source and of the RS can be reduced by 1.5 dB if the pilot power is equal to the signal power and by 3 dB if the pilot power is kept fixed, while maintaining a given quality-of-service. Furthermore, we compare FD and half-duplex (HD) modes and show that FD improves significantly the performance when the LI level is low.

We examine the impact of primary and secondary interference on opportunistic relaying in cognitive spectrum sharing networks. In particular, new closed-form exact and asymptotic expressions for the outage probability of cognitive opportunistic relaying are derived over Rayleigh and Nakagami-m fading channels. Our analysis presents revealing insights into the diversity and array gains, diversity-multiplexing tradeoff, impact of primary transceivers' positions, and the optimal position of relays. We highlight that cognitive opportunistic relaying achieves the full diversity gain which is a product of the number of relays and the minimum Nakagami-m fading parameter in the secondary network. Furthermore, we confirm that the diversity gain reduces to zero when the peak interference constraint in the secondary network is proportional to the interference power from the primary network.

Radio-frequency (RF) impairments in the transceiver hardware of communication systems (e.g., phase noise (PN), high power amplifier (HPA) nonlinearities, or in-phase/quadrature-phase (I/Q) imbalance) can severely degrade the performance of traditional multiple-input multiple-output (MIMO) systems. Although calibration algorithms can partially compensate these impairments, the remaining distortion still has substantial impact. Despite this, most prior works have not analyzed this type of distortion. In this paper, we investigate the impact of residual transceiver hardware impairments on the MIMO system performance. In particular, we consider a transceiver impairment model, which has been experimentally validated, and derive analytical ergodic capacity expressions for both exact and high signal-to-noise ratios (SNRs). We demonstrate that the capacity saturates in the high-SNR regime, thereby creating a finite capacity ceiling. We also present a linear approximation for the ergodic capacity in the low-SNR regime, and show that impairments have only a second-order impact on the capacity. Furthermore, we analyze the effect of transceiver impairments on large-scale MIMO systems; interestingly, we prove that if one increases the number of antennas at one side only, the capacity behaves similar to the finite-dimensional case. On the contrary, if the number of antennas on both sides increases with a fixed ratio, the capacity ceiling vanishes; thus, impairments cause only a bounded offset in the capacity compared to the ideal transceiver hardware case.

Radio-frequency (RF) impairments, that exist intimately in wireless communications systems, can severely degrade the performance of traditional multiple-input multiple-output (MIMO) systems. Although compensation schemes can cancel out part of these RF impairments, there still remains a certain amount of impairments. These residual impairments have fundamental impact on the MIMO system performance. However, most of the previous works have neglected this factor. In this paper, a training-based MIMO system with residual transmit RF impairments (RTRI) is considered. In particular, we derive a new channel estimator for the proposed model, and find that RTRI can create an irreducible estimation error floor. Moreover, we show that, in the presence of RTRI, the optimal training sequence length can be larger than the number of transmit antennas, especially in the low and high signal-to-noise ratio (SNR) regimes. An increase in the proposed approximated achievable rate is also observed by adopting the optimal training sequence length. When the training and data symbol powers are required to be equal, we demonstrate that, at high SNRs, systems with RTRI demand more training, whereas at low SNRs, such demands are nearly the same for all practical levels of RTRI.

This work studies the uplink of a cellular network with zero-forcing (ZF) receivers under imperfect channel state information at the base station. More specifically, apart from the pilot contamination, we investigate the effect of time variation of the channel due to the relative users' movement with regard to the base station. Our contributions include analytical expressions for the sum-rate with finite number of BS antennas, and also the asymptotic limits with infinite power and number of BS antennas, respectively. The numerical results provide interesting insights on how the user mobility degrades the system performance which extends previous results in the literature.

We consider a multipair decode-and-forward relay channel, where multiple sources transmit simultaneously their signals to multiple destinations with the help of a full-duplex relay station. We assume that the relay station is equipped with massive arrays, while all sources and destinations have a single antenna. The relay station uses channel estimates obtained from received pilots and zero-forcing (ZF) or maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To significantly reduce the loop interference effect, we propose two techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the relay station. We derive an exact achievable rate expression in closed-form for MRC/MRT processing and an analytical approximation of the achievable rate for ZF processing. This approximation is very tight, particularly for a large number of relay station antennas. These closed-form expressions enable us to determine the regions where the full-duplex mode outperforms the half-duplex mode, as well as to design an optimal power allocation scheme. This optimal power allocation scheme aims to maximize the energy efficiency for a given sum spectral efficiency and under peak power constraints at the relay station and sources. Numerical results verify the effectiveness of the optimal power allocation scheme. Furthermore, we show that, by doubling the number of transmit/receive antennas at the relay station, the transmit power of each source and of the relay station can be reduced by 1.5 dB if the pilot power is equal to the signal power, and by 3 dB if the pilot power is kept fixed, while maintaining a given quality of service.

We analyze the performance of amplify-and-forward dual-hop relaying systems in the presence of in-phase and quadrature-phase imbalance (IQI) at the relay node. In particular, an exact analytical expression for and tight lower bounds on the outage probability are derived over independent, non-identically distributed Nakagami-m fading channels. Moreover, tractable upper and lower bounds on the ergodic capacity are presented at arbitrary signal-to-noise ratios (SNRs). Some special cases of practical interest (e.g., Rayleigh and Nakagami-0.5 fading) are also studied. An asymptotic analysis is performed in the high SNR regime, where we observe that IQI results in a ceiling effect on the signal-to-interference-plus-noise ratio (SINR), which depends only on the level of I/Q impairments, i.e., the joint image rejection ratio. Finally, the optimal I/Q amplitude and phase mismatch parameters are provided for maximizing the SINR ceiling, thus improving the system performance. An interesting observation is that, under a fixed total phase mismatch constraint, it is optimal to have the same level of transmitter (TX) and receiver (RX) phase mismatch at the relay node, while the optimal values for the TX and RX amplitude mismatch should be inversely proportional to each other.

We analyze the performance of dual-hop two-way amplify-and-forward relaying in the presence of in-phase and quadrature-phase imbalance (IQI) at the relay node. In particular, two power allocation schemes, namely, fixed power allocation and instantaneous power allocation, are proposed to improve the system reliability and robustness against IQI under a total transmit power constraint. For each proposed scheme, the outage probability is investigated over independent, non-identically distributed Nakagami- m fading channels, and exact closed-form expressions and bounds are derived. Our theoretical analysis indicates that, without IQI compensation, IQI can create fundamental performance limits on two-way relaying. However, these limits can be avoided by performing IQI compensation at source nodes. Compared with the equal power allocation scheme, our numerical results show that the two proposed power allocation schemes can significantly improve the outage performance, thus reducing the IQI effects, particularly when the total power budget is large.

A multiuser dual-hop relaying system over mixed radio frequency/free-space optical (RF/FSO) links is investigated. Specifically, the system consists of m single-antenna sources, a relay node equipped with n≥ m receive antennas and a single photo-aperture transmitter, and one destination equipped with a single photo-detector. RF links are used for the simultaneous data transmission from multiple sources to the relay. The relay operates under the decode-and-forward protocol and utilizes the popular V-BLAST technique by successively decoding each user's transmitted stream. Two common norm-based orderings are adopted, i.e., the streams are decoded in an ascending or a descending order. After V-BLAST, the relay retransmits the decoded information to the destination via a point-to-point FSO link in m consecutive timeslots. Analytical expressions for the end-to-end outage probability and average symbol error probability of each user are derived, while closed-form asymptotic expressions are also presented. Capitalizing on the derived results, some engineering insights are manifested, such as the coding and diversity gain of each user, the impact of the pointing error displacement on the FSO link and the V-BLAST ordering effectiveness at the relay.

Two closed-form expressions for the end-to-end cumulative distribution function (CDF) and outage probability of two-way interference-limited amplify-and-forward (AF) relaying were recently presented in Equations (19) and (24) in the above titled paper (ibid., vol. 61, no. 8 pp. 3156-3169, Aug. 2013). However, the expressions contain a notational error. A correction is presented here.

Previous works on cognitive relay networks (CRNs) considered only symmetric fading channels. However, in practical wireless propagation scenarios, it is likely that the channels of the secondary user (SU) and primary user (PU) may undergo different fading characteristics. In this paper, we assume that the channels of the secondary network (SU-source→SU-relay→SU-destination) are subject to Rician fading, whereas the channels of the link from the SU to the PU experience Rayleigh fading. Based on this framework, the end-to-end outage probability (OP) of CRNs is investigated for two different relaying schemes: i) in the absence of the direct link with decode-and-forward (DF) protocol and ii) in the presence of the direct link with incremental DF protocol. In particular, we derive both exact and asymptotic OP expressions for the considered CRNs. Our analysis reveals important insights into the impact of fading parameters on the CRN performance under distinct fading distributions.

We investigate the performance of two-way interference-limited amplify-and-forward relaying systems over independent but non-identically distributed (i.n.i.d.) Nakagami-m fading channels. A tight closed-form lower bound on the outage probability of the system is derived, along with a simplified expression in the asymptotically low outage regime. Some special cases of practical interest (e.g., no interference power and interference-limited case) are also examined. The theoretical and numerical results provide important physical insights into the implications of the model parameters on the system performance.

In this paper, we study the achievable uplink rates of massive multiple-input multiple-output (MIMO) systems using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming imperfect channel state information (CSI). Unlike all previous studies, the fast fading MIMO channel matrix here is modeled to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. In particular, it is found that with a non-zero Ricean K-factor, the approximations and the exact uplink rates converge to the same constant value if the number of base station antennas, M, grows large, while the transmit power of each user is scaled down proportionally to 1/M. However, if the channel is Rayleigh fading, we can only cut the transmit power of each user proportionally to 1/√M. In addition, we show that with increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

Recently, it was shown that transceiver hardware impairments have a detrimental impact on the performance of communication systems, especially for high-rate systems. The vast majority of technical contributions in the area of relaying assume ideal transceiver hardware. This paper quantifies the impact of transceiver hardware impairments in dual-hop Amplify-and-Forward (AF) relaying, both for fixed and variable gain relays. The outage probability (OP) in this practical scenario is a function of the instantaneous end-to-end signal-to-noise-and-distortion ratio (SNDR). This paper derives closed-form expressions for the exact and asymptotic OPs under Rayleigh fading, accounting for hardware impairments at both the transmitter and the relay. The performance loss is small at low spectral efficiency, but can otherwise be very substantial. In particular, it turns out that for high signal-to-noise ratio (SNR), the instantaneous end-to-end SNDR converges to a deterministic constant, called the SNDR ceiling, which is inversely proportional to the level of impairments. This stands in stark contrast to the ideal hardware case for which the end-to-end SNDR grows without bound in the high SNR regime.

We investigate the performance of two-way interference-limited amplify-and-forward (AF) relaying systems with selection-combining (SC) over Nakagami-m fading channels. In particular, a tight lower bound on the end-to-end outage probability (OP) is derived in closed-form, while a useful expression is presented for the asymptotically low outage regime. Some special cases of practical interest (e.g., no interference power and Rayleigh fading channels) are also studied. The numerical results provide important physical insights into the implications of model parameters on the system performance.

We analyze the performance of a dual-hop selection combining amplify-and-forward cooperative system over independent and identically distributed Nakagami-m fading channels, where multiple antennas are deployed in the receive side of relays and the destination. The outage probability, symbol error probability and average channel capacity are derived at arbitrary signal to noise ratios (SNRs). In order to obtain additional physical insights, we derive the above mentioned performance metrics in the high SNR regime; this enables us to parameterize the performance of the system in terms of diversity order and coding gain. Furthermore, some special cases of interest (e.g., Nakagami-0.5 and Rayleigh fading channels) are also studied. It is demonstrated that the analytical expressions, expressed via infinite series, match precisely with the Monte-Carlo simulations using only a small number of terms.

The Gallager's random coding error exponent for space-time block codes (STBC) over multiple-input multiple-output (MIMO) block-fading channels, with Gaussian input distribution, is investigated. Gallager's error exponent can be used to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We first provide new, analytical expressions for Gallager's exponent of STBC systems over η-μ fading channels. The Shannon capacity and cutoff rate, which can be directly derived from Gallager's exponent, are further examined. In order to get additional insights, a high signal-to-noise ratio analysis is pursued to investigate the effects of coherence time and codeword length on the error probability. For the sake of completeness, we provide the link to previous known results on Rayleigh and Nakagami-m fading channels.

In this paper, we analytically investigate the achievable rate of multiple-input single-output (MISO) channels in the presence of delay constraints. In particular, we focus on the so-called effective rate which was recently established as a suitable metric for assessing the impact of delay constraints on the overall performance of communication systems. Yet, most prior relevant works have considered only the typical cases of Rayleigh, Rician and Nakagami-m fading which allow for tractable manipulations. In this paper, we relax this assumption by considering MISO systems over η-μ fading channels. The η-μ distribution has been shown to provide very good fit to experimental data in various propagation environments. New, analytical expressions for the exact effective rate are derived. Moreover, we consider the asymptotically high and low signal-to-noise (SNR) regimes, for which tractable, closed-form effective rate expressions are presented. These results enable us to explicitly investigate the impact of system parameters on the effective rate of MISO η-μ fading channels.

The papers in this special issue focus on large-scale multiple antenna wireless systems and services.

The performance of single-cell distributed multiple-input multiple-output (D-MIMO) systems is not only affected by small-scale Rayleigh fading but also from large-scale fading and path-loss. In this paper, we elaborate on the sum rate of D-MIMO systems employing linear zero-forcing receivers, accounting for both large and small-scale fading effects, as well as spatial correlation at the transmit side. In particular, we consider the classical lognormal model and propose closed-form upper and lower bounds on the achievable sum rate. Using these bounds as a starting point, we pursue a "large-system" analysis and provide asymptotic expressions when the number of antennas at the base station (BS) grow large, and when the number of antennas at both ends grow large with a fixed and finite ratio. A detailed characterization in the asymptotically high and low signal to noise ratio regimes is also provided. An interesting observation from our results is that in order to maximize the sum rate, the RPs should be placed at unequal distances to the BS when they experience the same level of shadowing. The resulting closed-form expressions are compared with the corresponding results on MIMO optimal receivers.

Physical transceivers have hardware impairments that create distortions which degrade the performance of communication systems. The vast majority of technical contributions in the area of relaying neglect hardware impairments and, thus, assume ideal hardware. Such approximations make sense in low-rate systems, but can lead to very misleading results when analyzing future high-rate systems. This paper quantifies the impact of hardware impairments on dual-hop relaying, for both amplify-and-forward and decode-and-forward protocols. The outage probability (OP) in these practical scenarios is a function of the effective end-to-end signal-to-noise-and-distortion ratio (SNDR). This paper derives new closed-form expressions for the exact and asymptotic OPs, accounting for hardware impairments at the source, relay, and destination. A similar analysis for the ergodic capacity is also pursued, resulting in new upper bounds. We assume that both hops are subject to independent but non-identically distributed Nakagami-m fading. This paper validates that the performance loss is small at low rates, but otherwise can be very substantial. In particular, it is proved that for high signal-to-noise ratio (SNR), the end-to-end SNDR converges to a deterministic constant, coined the SNDR ceiling, which is inversely proportional to the level of impairments. This stands in contrast to the ideal hardware case in which the end-to-end SNDR grows without bound in the high-SNR regime. Finally, we provide fundamental design guidelines for selecting hardware that satisfies the requirements of a practical relaying system.

In this paper, we investigate the performance of two-way interference-limited amplify-and-forward relaying systems over independent, non-identically distributed Nakagami-m fading channels. Our analysis generalizes several previous results, since it accounts for interference affecting all network nodes. In particular, tight lower bounds on the end-to-end outage and symbol error probability are derived in closed-form, while a useful expression is presented for the asymptotically low outage regime. Some special cases of practical interest (e.g., no interference power and interference-limited case) are also studied. Using the derived lower bounds as a starting point and for the case of Rayleigh fading, we formulate and solve analytically three practical optimization problems, namely, power allocation under fixed location for the relay, optimal relay position with fixed power allocation, and joint optimization of power allocation and relay position under a transmit power constraint. The numerical results provide important physical insights into the implications of model parameters on the system performance; for instance, it is demonstrated that relay position optimization offers significant performance enhancement over the non-optimized case for an asymmetric interference power profile, whilst the optimization gains are marginal for a symmetric one.

We propose a multiuser downlink transmission scheme exploiting both statistical and outdated channel state information (CSI) at the transmitter. Based on the outdated CSI-aided transmission scheme introduced in (denoted as MAT), the proposed scheme reduces the original K-user MAT system to a two-user virtual MAT system, through statistical precoding in the first two transmission slots. Thus, the proposed scheme (denoted as V-MAT) reduces efficiently the implementation complexity, while increasing the achievable rate at finite signal-to-noise ratios (SNRs). For the V-MAT scheme, we derive an analytical high SNR rate approximation for correlated Rayleigh fading. Furthermore, for independent and identically distributed Rayleigh fading, we derive an exact rate expression at high SNRs, as well as a tight lower bound which applies for arbitrary SNRs. Then, precoder design is investigated, where an efficient near-optimal solution is proposed for arbitrary number of transmit antennas, and a closed-form optimal solution is derived for the two-antenna case. It is demonstrated that the proposed V-MAT scheme yields higher achievable rate than the original MAT scheme at practical SNRs. Moreover, by combining the V-MAT scheme and the generalized MAT scheme of , where precoding is implemented in the third transmission slot, the achievable rate can be further increased.

In this paper, we analytically investigate Gallager's exponent for space-time block codes over multiple-input multiple-output block-fading channels with Gaussian input distribution. As a suitable metric of the fundamental tradeoff between communication reliability and information rate, Gallager's exponent can be used to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We assume that the receiver has full channel state information (CSI), while the transmitter has no CSI and performs equal power allocation across all transmit antennas. In the following, novel exact expressions for Gallager's exponent are derived for two well-known channel fading models, namely η-μ and κ-μ fading models. More importantly, the implications of fading parameters and channel coherence time on Gallager's exponent are investigated. In addition, we present new expressions for the Shannon capacity, cutoff rate and expurgated exponent for the above mentioned fading models, while in the high signal-to-noise ratio regime, simplified closed-form expressions are also derived. Finally, we highlight the fact that the presented analysis encompasses all previously known results on Nakagami-m, Rician, Rayleigh and Hoyt fading channels, as special cases.

In this paper, we pursue a performance evaluation of distributed multiple-input multiple-output (MIMO) systems in composite Rayleigh/Inverse-Gaussian fading channels. Capitalizing on some generic bounding techniques, we first derive new closed-form bounds on the ergodic capacity of optimal receivers. In order to gain useful insights into the impact of fading parameters on optimal receivers' performance, a detailed characterization in the asymptotically high and low signal-to-noise ratio regimes is also provided. In addition, we explore the “large-system” regime and provide asymptotic expressions when the number of antennas grows large. A similar performance analysis is performed for the achievable sum rate of distributed MIMO systems employing linear minimum mean-square error receivers.

This paper presents a detailed sum rate characterization of distributed multiple-input multiple-output systems operating over composite fading channels and employing linear zero-forcing receivers. We consider the Rayleigh/Lognormal fading model and also take into account the effects of path-loss and spatial correlation at the transmit side. New closed-form upper and lower bounds on the achievable sum rate are proposed that apply for arbitrary numbers of antennas. Moreover, we investigate the concept of large-scale multiple-antenna systems when the number of receive antennas grow large. In this asymptotic regime, it is shown that the effects of Rayleigh fading are averaged out and the channel is dominated by the much more slowly varying shadowing. An interesting observation from our results is that in order to maximize capacity, the radio ports should be placed at unequal distances to the base station when they experience the same level of shadowing.

We study the downlink of a multicell MIMO system where clusters of multi-antenna base stations jointly serve multiple single-antenna users, commonly referred to as a network MIMO system. Most of the previous studies on network MIMO have only considered the azimuth pattern of the antenna, while ignoring the elevation pattern. In this paper, we consider both the azimuth and the elevation patterns and investigate the impact of the elevation angle tuning parameter, denoted as the antenna tilt, on the performance of such systems. Using system simulations, it is shown that the promised performance gains of network MIMO systems over conventional non-coordinated systems, crucially depend on the choice of the right tilt setting including the tilt type, i.e., mechanical or electrical, and the tilt angle. In particular, for tilt angles smaller than the optimum, network MIMO with intra-site coordination performs almost as well as the conventional system; while for tilt angles larger than the optimum, the performance of network MIMO with intra-site is similar to that of network MIMO with inter-site coordination.

In this paper, we investigate the problem of channel estimation in amplify-and-forward multiple-input multiple-output relaying systems operating over random wireless channels. Using the Bayesian framework, novel linear minimum mean square error and expectation-maximization based maximum a posteriori channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. Moreover, new, explicit expressions for the Bayesian Cramer-Rao bound are deduced for predicting and evaluating the channel estimation accuracy. Our simulation results demonstrate that the incorporation of prior knowledge into the channel estimation algorithm offers significantly improved performance, especially in the low signal-to-noise ratio regime.

The delay constraints imposed by future wireless applications require a suitable metric for assessing their impact on the overall system performance. Since the classical Shannon's ergodic capacity fails to do so, the so-called effective rate was recently established as a rigorous alternative. While prior relevant works have improved our knowledge on the effective rate characterization of communication systems, an analytical framework encompassing several fading models of interest is not yet available. In this paper, we pursue a detailed effective rate analysis of Nakagami-m, Rician and generalized-K multiple-input single-output (MISO) fading channels by deriving new, analytical expressions for their exact effective rate. Moreover, we consider the asymptotically low and high signal-to-noise regimes, for which tractable, closed-form effective rate expressions are presented. These results enable us to draw useful conclusions about the impact of system parameters on the effective rate of different MISO fading channels. All the theoretical expressions are validated via Monte-Carlo simulations.

We investigate the ergodic sum rate of multiple-input multiple-output Nakagami-m fading channels with linear receivers. In particular, both mean square error and zero-forcing receivers are considered. For dual transmit antenna configurations, we present new, closed-form upper bounds on the ergodic sum rate of both receivers. Moreover, we derive exact expressions for the two key parameters dictating the sum rate behavior in the low signal to noise ratio regime, namely the minimum energy per information bit to reliably convey any positive rate and the wideband slope. By doing so, we are able to explicitly demonstrate the sub-optimality of linear receivers compared to optimal receivers, and draw useful insights into the impact of model parameters (e.g., number of antennas, fading parameters).

In this paper, we introduce two novel upper bounds on the achievable sum rate of multiple-input multiple-output (MIMO) systems with Zero-Forcing (ZF) receivers. The presented bounds are given in tractable closed-form and apply for different fading models, like uncorrelated/doubly correlated Rayleigh fading and Ricean fading. In addition, the first bound establishes an interesting relationship between the sum rate and the first negative moment of the unordered eigenvalue of the instantaneous correlation matrix. Based on our analytical expressions, we are able to explore the impact of the model parameters, such as number of antennas, spatial correlation and Ricean-K factor, on the sum rate of MIMO ZF receivers.

This paper presents a detailed sum rate investigation of Zero-Forcing (ZF) detectors over composite multiple-input multiple-output (MIMO) channels. To this end, we consider the generic K distribution (Rayleigh/gamma distribution) to model the composite fading fluctuations and also assume the general case of semi-correlated small-scale fading. Novel exact analytical expressions are derived for the achievable sum rate followed by asymptotic expressions in the low Signal to-Noise ratio (SNR) regime. In parallel, new, closed-form upper and lower bounds on the sum rate are derived that re main tight for all SNRs. The theoretical analysis is validated via a set of Monte-Carlo simulations.

We propose a novel, coordinated user scheduling (CUS) algorithm for inter-cell interference (ICI) mitigation in the downlink of a multi-cell multi-user MIMO system. In the proposed algorithm, ICI mitigation is performed through the exchange of necessary channel state information (CSI) among the base stations, and the revision of the scheduling decisions and beamformer designs at each base station. Furthermore, ICI mitigation is performed only for the cell-edge users so that the amount of inter-base station signaling overhead is minimized. Our simulation results demonstrate that the proposed coordination scheduling algorithm significantly improves the cell-edge users' throughput compared to conventional systems with only a negligible amount of CSI sharing among the base stations and a relatively small throughput loss for the cell-interior users.

We present a novel architecture for hybrid radio frequency (RF)/ free space optical (FSO) wireless systems without feedback or channel state information (CSI) at the transmitter. Under the assumption that 60 GHz RF and FSO systems support the same data rates, the proposed implementation transmits the same data over both links, using phase shift keying (PSK) as a common modulation scheme, and combines the signals from each individual link at the receiver on a symbol-by-symbol basis. Two popular diversity combining schemes are considered, namely, selection combining (SC) and maximal ratio combining (MRC), while tractable analytical approximations for the bit error rate (BER) are obtained. Investigations over various weather conditions and link distances revealed that the proposed implementation fully exploits the complementary nature of RF and FSO channels, even when one of the two available links fails. Furthermore, the comparison of the combining schemes demonstrates MRC as the optimum combining scheme, offering link distance gains compared to SC.

In this paper, we consider the fundamental problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over random channels. Using the Bayesian framework, linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. The performance of the proposed algorithms is evaluated in terms of the mean square error (MSE) as a function of the signal-tonoise ratio (SNR) during the training interval. Our simulation results show that the incorporation of prior knowledge into the channel estimation algorithm offers improved performance, especially in the low SNR regime.

We present a novel and analytical lower bound on the ergodic capacity of distributed multiple-input multiple-output (D-MIMO) systems operating in composite Rayleigh/lognormal (RLN) fading and assuming double-sided spatial correlation. The proposed lower bound is applicable for finite number of antennas and remains tight across the entire Signal-to-Noise (SNR) regime. In addition, we perform a detailed low-SNR analysis that provides useful insights into the implications of the system parameters on MIMO capacity.

This paper provides a systematic characterization of Zero-Forcing (ZF) detectors over multiple-input multiple-output (MIMO) channels that experience both small and large-scale fading. In particular, we consider the generic K distribution (Rayleigh/gamma distribution) to model the composite fading fluctuations and also assume the general case of semi-correlated small-scale fading. In the following, novel exact analytical expressions for the achievable sum rate are derived, followed by asymptotic expressions in the high and low Signal-to-Noise ratio (SNR) regimes. In these limiting cases, two common and insightful affine expansions are studied followed by new, closed-form upper and lower bounds on the sum rate that remain tight for all SNRs. In the second part of the paper, we present exact tractable expressions along with first-order expansions for the symbol error rate (SER) and outage probability; we also quantify the performance of ZF detectors in terms of diversity order and array (or coding) gain. The implications of the model parameters on the ZF detector performance are investigated via Monte-Carlo simulations which also validate the theoretical analysis.

In this paper, we present a general analytical framework for the exact mutual information (MI) cumulants of multiple-input multiple-output (MIMO) systems with perfect receiver channel state information (CSI) and no transmitter CSI. Our derivation is based on a recent parameterization of the joint ordered eigenvalue probability density function (PDF), that encompasses both uncorrelated/semi-correlated Rayleigh channels as well as uncorrelated Rician channels. In addition, we extend our framework to account for the cumulants of doubly-correlated Rayleigh channels and also to deduce tractable expressions in the high Signal-to-Noise ratio (SNR) regime. The cumulants are particularly useful to study all high-order statistics (HOS) of the MI; in fact, they can be used to express the MI mean and variance as a finite sum of determinants. Our analytical expressions are then validated via Monte-Carlo simulations with the attained accuracy being excellent in all cases.

In this paper, we propose the Inverse Gaussian (IG) distribution, as a less complex alternative to Log-normal (LN), to describe turbulence-induced fading in free-space optical (FSO) systems operating in weak turbulence conditions and/or in the presence of a large amount of aperture averaging. By conducting goodness of fit tests, we define the range of values of the scintillation index, where the two distributions approximate each other, with a certain significance level. The efficiency of the new model is pointed out by deriving analytical expressions for the calculation of the bit-error rate of two typical FSO systems; an intensity-modulation/direct detection FSO system with M-ary pulse position modulation and a heterodyne FSO system with differential phase shift keying. Numerical examples are provided to clearly illustrate the accuracy of the proposed approach in the weak turbulence regime.

The standard condition number (SCN) is a fundamental metric in the context of multiple-input multiple-output communication systems, linear detection and classical linear algebra. Hence, in this paper we propose a novel generic framework for the SCN distribution of three different classes of Wishart matrices which leads to new results and insights. In particular, our analysis covers both central and non-central Wishart distributions of arbitrary dimension and therefore is applicable to uncorrelated/semi-correlated Rayleigh fading and Ricean fading scenarios. For the special case of dual semi-correlated central Wishart matrices, we derive exact and asymptotic polynomial expressions for the SCN distributions. All analytical results are validated via Monte-Carlo simulations with the attained accuracy being excellent in all cases. The impact of the model parameters on channel conditioning is also investigated in detail.

The presence of line-of-sight (LoS) components is typically considered as a hindrance for multiple-input multiple-output (MIMO) communications due to the limited amount of multipath scattering which, in turn, results in low spatial multiplexing gains. However, some recent investigations have questioned this common belief and demonstrated that by employing specifically designed antenna arrays at both the transmitter (Tx) and receiver (Rx), the mean channel matrix can become full-rank and, consequently, we can obtain high channel capacities even at high Ricean K-factors. In this paper, using the joint ordered eigenvalue probability density function (PDF) as a starting point, we derive analytical exact and asymptotic expressions for the mutual information (MI) statistics of these optimized LoS MIMO configurations. The proposed analytical formulae are given in a tractable determinant form and thus can be easily evaluated and efficiently programmed. The implications of the model parameters on MI statistics are also assessed with the match between the analytical curves and Monte-Carlo simulations being excellent.

This paper provides a systematic mutual information (MI) and multichannel beamforming (MBF) characterization of optimized multiple-input multiple-output (MIMO) communication systems operating in Ricean fading. These optimized configurations are of high practical importance since, contrary to the common belief, benefit from the presence of direct Line-of-Sight (LoS) components and deliver maximum multiplexing gains, by deploying specifically designed antenna arrays at both ends. In the following, using elements from random matrix theory, novel analytical expressions are derived for the exact and asymptotic MI statistics while the prevalent Gaussian approximation is examined. Moreover, new explicit expressions for the marginal eigenvalues are deduced which are thereafter used to analyze the BF performance of the associated eigenmodes in terms of Signal-to-Noise ratio (SNR) outage probability. We note that all derived formulas are given in tractable determinant form and therefore allow for fast and efficient computation and also yield an excellent match with Monte-Carlo simulations, under different fading scenarios and model parameters.

This paper investigates the distribution of the condition number of complex Wishart matrices. Two closely related measures are considered: the standard condition number (SCN) and the Demmel condition number (DCN), both of which have important applications in the context of multiple-input multiple-output (MIMO) communication systems, as well as in various branches of mathematics. We first present a novel generic framework for the SCN distribution which accounts for both central and non-central Wishart matrices of arbitrary dimension. This result is a simple unified expression which involves only a single scalar integral, and therefore allows for fast and efficient computation. For the case of dual Wishart matrices, we derive new exact polynomial expressions for both the SCN and DCN distributions. We also formulate a new closed-form expression for the tail SCN distribution which applies for correlated central Wishart matrices of arbitrary dimension and demonstrates an interesting connection to the maximum eigenvalue moments of Wishart matrices of smaller dimension. Based on our analytical results, we gain valuable insights into the statistical behavior of the channel conditioning for various MIMO fading scenarios, such as uncorrelated/semi-correlated Rayleigh fading and Ricean fading.

In this paper, we explore the concept of inner and outer functions to come up with two novel parametric families of Nyquist pulses. Aside from requiring only two design parameters, the proposed pulses yield an enhanced performance compared to the sophisticated flipped-inverse hyperbolic secant (asech) pulse, that was recently presented in the literature. While the construction of parametric families originates from the work of Beaulieu and Damen, the usage of inner and outer functions guarantees a higher flexibility in the choice of the composite family members. The proposed pulses may have a slower decay rate than the well-known raised-cosine (RC) pulse, but exhibit a more pronounced decrease in the amplitudes of the two largest sidelobes and this accounts for their improved robustness to error probabilities. In the following, it is clearly demonstrated that a lower bit error rate (BER), compared to the existing pulses, can be achieved for different values of the roll-off factor and timing jitter. Moreover, a smaller maximum distortion along with a more open eye diagram are attained as well.

An analytical upper bound on the ergodic capacity of Multiple-Input Multiple-Output (MIMO) systems is deduced with the aid of a simplified approach that relies on a fundamental power normalization. Given their high practical usability, we are particularly interested in dual configurations where both ends deploy two antenna elements. Contrary to the majority of related studies, where only the common case of Rayleigh fading is considered, our analysis is extended to account for Ricean fading where a deterministic Line-of-Sight (LoS) component exists in the communication link and both ends are affected by spatial correlation. In the following, it is shown that the proposed bound is applicable for any arbitrary Signal-to-Noise Ratio (SNR) and rank of the mean channel matrix. In fact, we consider both conventional and optimized MIMO configurations with equal LoS eigenvalues. Moreover, the tightness of the bound is explored where it is demonstrated that as the SNR tends to zero the bound becomes asymptotically tight; at high SNRs, the offset between empirical capacity and the bound is analytically computed which implies that an explicit asymptotic capacity expression can ultimately be obtained.

In this paper, we explore the statistical characterization of Multiple-Input Multiple-Output (MIMO) channel correlation matrices with the main focus being on their condition number statistics. More specifically, novel expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the MIMO condition number. Contrary to the majority of related studies, where only the common case of Rayleigh fading was considered, our investigation is extended to account for the generalized case of Ricean fading where a deterministic Line-Of-Sight (LoS) component exists in the communication link. The overall analysis is based on the principles of random matrix theory and particularly of dual complex non-central Wishart matrices; the latter represent a practical class of MIMO systems, namely dual-branch systems which are equipped with two transmit and receive antenna elements. All the derived formulae are validated through extensive simulations with the attained accuracy being remarkably good.

In this paper, the performance of a multiple-input multiple-output (MIMO) system is assessed in the case of vehicle-to-roadside communications. We investigate a line-of- sight (LoS) scenario where a specular wavefront impinges on the receive side. Under these conditions, the channel response is usually rank deficient due to the high correlation between the spatial LoS responses, unless specific antenna geometries are employed in order to achieve subchannel orthogonality. For our investigation, a recently proposed criterion, which maximizes the LoS-channel rank and eventually the channel capacity, is revised for different array configurations.

Abstract In this paper, the performance of a dual Multiple‐Input Multiple‐Output (MIMO) communication system is investigated using data obtained from an indoor measurement campaign. We focus on the double‐directional channel response of each frequency band and the degree of similarity between them. In general, when a Frequency Division Duplex (FDD) system is employed, as for example in Universal Mobile Telecommunications System (UMTS), it is of vital importance to study whether the measurement results from the uplink could be directly extrapolated to the downlink direction, and vice versa. By applying two conventional beamforming techniques and the well known MUltiple SIgnal Classification (MUSIC) algorithm, the angular domain performance of both bands was assessed. The initial results presented herein are promising enough as the joint spectra seem to be remarkably similar implying that the underlying propagation mechanisms at both bands are also similar. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper, the frequency domain (FD) SAGE (Space-Alternating Generalized Expectation-maximization) algorithm has been extended to the MIMO case in order to determine the angular and temporal channel characteristics at both ends of the radio link. The implementation of the SAGE algorithm relies on the serial interference cancellation(SIC) technique which outperforms the conventional parallel interference cancellation (PIC) scheme when used in the frequency domain, especially in the common case of unequal power levels. The main purpose of our investigation has been twofold. Firstly, a synthetic environment was generated to testify the efficiency of the proposed algorithm in a severe multipath indoor scenario. Secondly, a measurement campaign was conducted and the obtained data were post- processed in order to assess the whole double-directional domain. The results revealed that the proposed algorithm demonstrates a stable performance and robustness as well as rapid convergence which are the principal criteria an estimation technique must fulfill.