Compact ultra-massive antenna arrays (CUMA) share key characteristics with holographic communication systems, featuring densely spaced and individually controlled antenna elements that enable precise manipulation of electromagnetic waves. In this paper, we investigate the spectral efficiency (SE) of CUMA deployed within some constrained physical space. Departing from prior works that assume ideal isotropic antennas, we derive a closed-form expression for the SE assuming a line-of-sight (LoS) channel at the electromagnetic level, explicitly accounting for mutual coupling and antenna orientation. The analysis reveals that the channel gain is highly sensitive to both the array orientation and individual antenna directions. In the single-user case, our results show that the optimal orientation of the user array is either aligned parallel or perpendicular to the signal direction, depending on the inter-element spacing. Notably, near-optimal channel gain is achieved when individual antennas are oriented perpendicular to the signal direction. In the multi-user case, we further optimize transceiver configurations under mutual coupling constraints. Simulation results confirm that SE is strongly influenced by the directional alignment of user antennas and array placement in the near-field regime. CUMA significantly outperforms traditional half-wavelength spaced arrays in terms of SE when constrained to the same physical aperture.

This paper investigates the impact of imperfect visibility region (VR) estimation on the energy efficiency (EE) of active extra large-reconfigurable intelligent surface (XL-RIS)-aided spatially non-stationary Internet of Things (IoT) systems. Unlike existing VR-aware XL-RIS studies that either assume perfect VR knowledge or evaluate system performance under a given detected VR pattern, we consider a practical VR acquisition process with detection errors and quantitatively analyze the impact of VR errors on the ergodic performance. Specifically, we first propose an uplink VR estimation scheme in which only elements within the true VR can receive user signals, and derive the false detection and missed detection probabilities by analyzing the accumulated pilot’s energy distribution. In the downlink, only the estimated VR elements are activated for reflection, and the base station applies conjugate beamforming based on the estimated cascaded channel information. A deterministic approximation for the ergodic average EE is then obtained, enabling an alternate optimization of the VR detection threshold, pilot length, transmit power, the active RIS phase-shift and amplification factor. Moreover, an efficient EE approximation by taking a few dominant VR detection outcomes is further developed, reducing the computational complexity significantly. Simulation results show that accurate VR estimation is crucial for realizing the performance gains of XL-RIS, and the proposed scheme achieves EE close to that with perfect VR knowledge.

With the deployment of the extremely large aperture arrays (ELAA), spatial non-stationarities start to emerge, implying that user signals can only be available to a portion of the base station (BS) antenna array, termed the visibility regions (VR). The existence of VR poses significant challenges to the design of transmission schemes in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. In this paper, we investigate the uplink transmission of XL-MIMO system to evaluate the impact of imperfect VR information on the reliability of XL-MIMO detection. Firstly, an energy detector is introduced to estimate the VR during the pilot training phase, and then channel estimation and signal detection are performed on the estimated VR. Subsequently, based on the estimated VR channel, we derive the distribution of signal-to-interference-noise ratio (SINR) and the average bit error rate (BER) with a linear receiver under different modulation schemes. Furthermore, the average BER is minimized by optimizing the VR detection threshold. Simulations show that VR estimation not only significantly reduces the complexity of channel estimation and signal detection but also improves the average BER performance.

This letter investigates spatial non-stationarities at both the reconfigurable intelligent surface (RIS) and base station, where only partial arrays, referred to as the visibility region (VR), can receive user signals. To improve spectral efficiency (SE) in frequency division duplexing systems, we embed VR estimation into the uplink training phase, which reduces downlink training overhead by merely activating VR elements. Specifically, VR estimation is separately accomplished via energy detection at the base station and RIS. A deterministic approximation of the average ergodic SE is then derived using large-system analysis, whereas its complexity remains prohibitive due to exhaustive enumeration of all possible VR detection results. To address this, we further approximate the average SE by collecting only dominant detection results, thus reducing complexity significantly. Moreover, we leverage historical pilot energy over multiple coherence times to enhance VR detection accuracy. By maximizing the low-complexity approximation of average SE, we alternately optimize detection thresholds, pilot length for VR training, and the downlink RIS phase-shift matrix. Numerical results validate that the proposed scheme improves VR detection accuracy and can closely approach the SE performance of perfect VR knowledge.

The rapid evolution of 6th generation mobile networks (6G), particularly with extremely large-scale multiple-input multiple-output (XL-MIMO) technology, presents new opportunities and challenges for the realization of the metaverse. This paper adopts visibility region (VR) modeling to capture the spatially non-stationary characteristics of XL-MIMO, and proposes a grouped VR estimation scheme based on pilot energy detection to effectively reduce the computational complexity of channel estimation and signal processing. The efficiency and robustness of VR estimation are enhanced by estimating VR states of several contiguous antennas simultaneously, and then the VR estimation probabilities of each subarray and the entire antenna array are derived. Simulation results demonstrate that the proposed grouped VR estimation with a well-designed detection threshold can effectively lower the system’s average bit error rate (BER), making it a promising solution for large-scale communication in immersive 6G applications.

For a Reconfigurable Intelligent Surface (RIS)-assisted Multiple Input Multiple Output (MIMO) Non-Orthogonal Multiple Access (NOMA) downlink system, the transmit covariances matrix at the base station and the phase-shifting matrix at the RIS are jointly designed based on statistical Channel State Information (CSI). First, in the spatial correlated Rician channel, a deterministic large-system approximation for the ergodic sum rate is obtained for an RIS-assisted MIMO-NOMA system by resorting to the large-dimensional random matrix theory. Then, by maximizing the approximated sum rate, the transmit covariances matrix for the strong and weak user as well as the phase-shifting matrix at the RIS are designed based on statistical CSI under the constraints of total transmit power and the rates threshold for weak user. The simulations validate the high accuracy of our approximations and our proposed transmit covariances and phase-shifting matrix can improve the system performances significantly.

By decoupling the dedicated radio frequency (RF) chain into transmit RF (TX RF) chain and receive RF (RX RF) chain, the asymmetrical system can flexibly equip the downlink/uplink array with different number of TX/RX RF chain according to the practical demand in a massive multiple-input multiple-output Internet of Things (IoT) network. To reduce cost and power consumption, this paper maximizes the uplink resource efficiency (RE) under Weichselberger channel model by designing transmit covariance matrices and receive antenna selection (RAS). In IoT networks with multiple IoT nodes, we propose an alternate optimization algorithm to iteratively optimize transmit covariance matrices and RAS by exploiting statistical channel state information. Specifically, for correlated channels, we propose a penalty method-based algorithm for RAS which utilizes Dinkelbach's transform and linear relaxation to tackle the intractable fractional function and binary constrain, respectively. Compared with greedy search, the proposed algorithm has lower complexity without much loss of performance. For independent identically distributed channels, we simplify the RE maximization problem and provide the necessary conditions of the optimal number of receive antennas and transmit power. Finally, the validness of our conclusions as well as the effectiveness of proposed algorithms are illustrated by numerical simulations.

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multi-user XL-MIMO scenario, where only partial antennas can receive users' signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

The orthogonal time frequency space (OTFS) is poised to become a pivotal technology for the next generation of mobile communications, due to its inherent robustness against Doppler shift. By combining OTFS technology with massive multiple-input multiple-output (MIMO) technology, users can experience high-quality communication services even in highly mobile scenarios. In this letter, we extend the massive MIMO-OTFS system to an asymmetrical architecture with unequal number of transceiver radio frequency chains. To overcome the channel inconsistency and recover the downlink channel by partial uplink channel, we utilize coprime patterns and propose a channel estimation algorithm that firstly extracts the angle parameters from the virtual array and then estimates the remaining channel parameters, which effectively reduces the three-dimensional search space to two dimensions. Our numerical simulations demonstrate that the proposed algorithm enhances the accuracy of channel estimation with much lower complexity.

This paper investigates a reconfigurable intelligent surface (RIS)-aided underlay cognitive radio (CR) multiple-input multiple-output (MIMO) wiretap channel where the secondary transmitter (ST) communicates with primary user (PU) and secondary user (SU) in the absence of the eavesdropper's (Eve's) channel state information (CSI). To enhance the secrecy performance in CR MIMO wiretap channel, the power of useful signal is minimized at ST, and then the residual power is further utilized to design artificial noise (AN) based on statistical CSI at ST. Specifically, we first optimize the transmit covariance matrix at ST and the diagonal phase-shifting matrix at RIS jointly leveraging large-system approximation results. Then the power allocation for SU is optimized to obtain the minimum transmit power of useful information at ST. Besides, we further design AN with the residual power by aligning it into the null space of the SU channel and thus avert the harmful effects of AN to improve the secure communication quality of SU. Finally, through numerical simulations, we illustrate the effectiveness of the proposed algorithm and validate the existence of a trade-off between the quality-of-service (QoS) at SU and secrecy rate.

In an extra large scale Multiple-Input Multiple-Output(MIMO) system where the Visibility Regions(VR) of different users are overlapping, the ergodic sum-rate is maximized by designing power allocation. Specifically, one base station equipped with an extra large scale array serves multiple users equipped with single-antenna, and their VRs are overlapped with adjacent users'. To reduce the inter-users interference and precoding complexity, the base station array is divided into several subarrays by the VR distributions, and then the regularized zero forcing precoding is employed for different subarray respectively. Furthermore, by exploiting the statistical channel state information, an approximation of the ergodic sum-rate is derived based on the large-dimensional random matrix theory. Based on the approximations, an optimal power allocation solution for different users is given in closed-form. Simulations illustrate that the proposed approximation fits the ergodic results well, and the proposed power allocation method can effectively improve system performances.

The reconfigurable intelligent surface (RIS), which is composed of multiple passive reflective components, is now considered as an effective mean to improve security performance in wireless communications, as it can enhance the signal of legitimate users and suppress the power leakage at eavesdroppers by adjusting signal phases. In this paper, we maximize the downlink ergodic secrecy sum rate of a RIS-aided multi-user system over Rician fading channels, where we assume that only imperfect channel state information (CSI) is available at the base station (BS). Firstly, we obtain the deterministic approximate expression for the ergodic secrecy sum rate by resorting to the large-system approximation theory. Then the problem is formulated to maximize the downlink ergodic secrecy sum rate by optimizing the regularization coefficient of regularized zero-forcing (RZF) precoding and the phase-shifting matrix of the RIS. By using the particle swarm optimization (PSO) method, we propose an alternate optimization (AO) algorithm to solve this non-convex problem. Finally, the numerical simulations illustrate the accuracy of our large-system approximate expression as well as the effectiveness of the proposed algorithm.

This letter considers downlink wideband channel estimation in an asymmetrical full-digital system where the number of uplink receive radio frequency chains is less than that of the downlink. This channel inconsistency will reduce downlink channel estimation accuracy. To deal with this problem, we adopt the coprime array topology in uplink receive antennas. Then, two iterative algorithms which are based on alternating direction method of multipliers and block-sparsity orthogonal matching pursuit, respectively, are developed to rebuild downlink channel. Simulation results demonstrate the effectiveness of the proposed scheme in the asymmetrical full-digital system.

This paper maximizes the uplink spectral efficiency to jointly design the receive antenna selection and transmit covariance matrix for an asymmetrical large-scale multiple-input multiple-output (LS-MIMO) system with spatial correlated chan-nel. Specifically, the asymmetrical system connects the partial antennas to limited receive radio frequency (RF) chains to save cost while all antennas are connected to transmit RF chains to obtain better performance. By exploiting statistical channel state information, we optimize the receive antenna selection in spatial correlated channel by Frank-Wolfe (FW) algorithm and the transmit covariance matrix by water-filling (WF) algorithm alternately. The whole procedure is named as FW-WF algorithm, which obtains very close performance to the optimal exhaustive search method but with much lower complexity. By numerical simulations, we also find that the uniform antenna selection is nearly optimal with exponential correlation model.

Node Localization has very broad application prospects in the field of wireless sensor networks and the Internet of Things. Among these localization algorithms, Distance Vector-Hop (DV-Hop) has drawn widespread interest because it provides a simple, feasible and low-cost localization scheme for isotropic networks. However, in anisotropic networks with obstacles and network holes, the localization accuracy of DV-Hop drops sharply due to the fact that the relationship between node distance and hops is not always linear. In order to solve the distance hops error, it is usually necessary to use a weighting function to correct the distance relationship between the hops, and at the same time select a better anchor node to improve the positioning accuracy. In this paper, we propose a weighted least squares cycle optimization DV-hop localization algorithm, which reduces the localization error of nodes by fusing the ideas of optimal weight function and reference anchor selection. Simulation results show that this algorithm has higher localization accuracy without requiring too much communication overhead.

Compared with traditional network structure, cloud-radio access network (C-RAN) can provide higher capacity with lower latency transmission performance. Meanwhile, more user equipment will require more radio remote heads (RRHs) in the same region. This paper studies the cancellation of interference from adjacent RRHs and maximization for sum-rate based on the limitation that imperfect channel state information is known at RRHs in a downlink multiple-input multiple-output C-RAN scenario, where line-of-sight and scattered components are both considered. We adopt the mutual information tight lower bound to substitute channel capacity designing criterion for the exact form of the latter is hard to obtain. Owing to the non-convexity of the problem based on lower bound designing criterion, we transform the problem into a convex form and give the optimal structure of precoding and decoding matrices by proving the equivalence between the minimum mean square error and the original objective function. Numerical results show that the proposed iterative algorithm effectively improves the transmission capacity and eliminates coherent interference between RRHs by jointly precoding and decoding.

Vehicle-to-everything (V2X) is considered as one of the most important applications of future wireless communication networks. However, the Doppler effect caused by the vehicle mobility may seriously deteriorate the performance of the vehicular communication links, especially when the channels exhibit a large number of Doppler frequency offsets (DFOs). Orthogonal time frequency space (OTFS) is a new waveform designed in the delay-Doppler domain, and can effectively convert a doubly dispersive channel into an almost non-fading channel, which makes it very attractive for V2X communications. In this paper, we design a novel OTFS based receiver with multi-antennas to deal with the high-mobility challenges in V2X systems. We show that the multiple DFOs associated with multipaths can be separated with the high-spatial resolution provided by multi-antennas, which leads to an enhanced sparsity of the OTFS channel in the delay-Doppler domain and bears a potential to reduce the complexity of the message passing (MP) detection algorithm. Based on this observation, we further propose a joint MP-maximum ration combining (MRC) iterative detection for OTFS, where the integration of MRC significantly improves the convergence performance of the iteration and gains an excellent system error performance. Finally, we provide numerical simulation results to corroborate the superiorities of the proposed scheme.