Reconfigurable antenna technologies, such as fluid and movable antennas, exploit spatial diversity but have limited ability to mitigate large-scale path loss due to their confined adjustment range. Pinching-antenna (PA) systems address this limitation by enabling flexible antenna deployment along dielectric waveguides. However, conventional single-waveguide PA architectures suffer from inter-antenna radiation, increased inwaveguide attenuation, and practical maintenance challenges. The segmented waveguide-enabled pinching-antenna (SWAN) architecture alleviates these issues by dividing the waveguide into independent segments, yet its combining flexibility remains limited in multiuser scenarios with a constrained radio frequency (RF) chain budget. To overcome this limitation, this paper investigates a practical SWAN-assisted multiuser multiple-input single-output uplink system employing tri-hybrid combining, where low-resolution phase shifters, discrete PA positions, and both fully connected (FC) and partially connected (PC) architectures are considered. We formulate a sum-rate maximization problem that jointly optimizes the digital combiner, analog phase shifters, and PA activations. To solve this highly coupled non-convex problem, we develop a unified algorithmic framework based on cross-entropy optimization (CEO) that learns high-quality configurations via parameterized probability distributions. In addition, a low-complexity coordinate update algorithm (CUA) is proposed to achieve a favorable trade-off between computational overhead and system performance. Simulation results demonstrate that the proposed CEO-based design outperforms benchmark methods under both FC and PC architectures, while the CUA-based design achieves an attractive complexity-performance trade-off.

In this paper, we propose a novel framework that integrates fluid antenna systems (FASs) with reconfigurable intelligent surfaces (RISs) to enhance the performance of multiuser multiple-input single-output (MU-MISO) downlink systems. Unlike conventional approaches that assume continuous fluid antenna (FA) mobility and rely exclusively on passive reflective RISs, the proposed design addresses practical constraints by: (i) incorporating discrete FA positioning to accommodate hardware limitations; (ii) utilizing irregular RIS configurations with low-resolution phase shifts to enhance channel diversity; and (iii) deploying irregular, low-resolution simultaneous transmitting and reflecting RISs (STAR-RISs) to achieve full-space signal coverage. A unified optimization framework is developed to jointly design the transmit beamforming vectors, discrete FA positions, and RIS/STAR-RIS coefficients. The proposed solution combines regularized zero-forcing (RZF) precoding with a probabilistic learning mechanism based on the cross-entropy optimization method. Simulation results reveal that the proposed architecture significantly outperforms conventional fixed-antenna systems and purely reflective RIS configurations, leading to substantial gains in sum-rate performance.

A major challenge for uplink multiuser massive multiple-input and multiple-output (MIMO) systems is the data detection problem at the receiver due to the substantial increase in the dimensions of MIMO systems. The optimal maximum likelihood detector is impractical for such large wireless systems, because it suffers from exponential complexity in terms of the number of users. Therefore, suboptimal alternatives with reduced complexity, such as the linear minimum mean square error (LMMSE) detector, are necessary. However, the LMMSE detector still introduces high computational complexity, mainly caused by the computation of the Gram matrix and matrix inversion. To reduce the computational complexity of data detection while achieving satisfactory bit error rate (BER) performance, we initially proposed an iterative data detection algorithm that exploits the coordinate descent method (CDM)-based algorithmic framework for uplink multiuser massive MIMO systems. We then developed a reduced-complexity hardware implementation algorithm by leveraging the “one-at-a-time” update property of the CDM-based algorithmic framework. Simulation results revealed that the proposed CDM-based detector provides the same or improved BER performance than the classical LMMSE algorithm at a lower complexity for different test scenarios.

Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.

Base station (BS) cooperation can turn unwanted interference to useful signal energy for enhancing system performance. In the cooperative downlink, zero-forcing beamforming (ZFBF) with a simple scheduler is well known to obtain nearly the performance of the capacity-achieving dirty-paper coding. However, the centralized ZFBF approach is prohibitively complex as the network size grows. In this paper, we devise message passing algorithms for realizing the regularized ZFBF (RZFBF) in a distributed manner using belief propagation. In the proposed methods, the overall computational cost is decomposed into many smaller computation tasks carried out by groups of neighboring BSs and communication is only required between neighboring BSs. More importantly, some exchanged messages can be computed based on channel statistics rather than instantaneous channel state information, leading to significant reduction in computational complexity. Simulation results demonstrate that the proposed algorithms converge quickly to the exact RZFBF and much faster compared to conventional methods.

The conventional linear minimum mean square error (LMMSE) estimator is commonly implemented through the sample covariance matrix. This estimator can only be implemented if the sample size N is higher than the observation dimension M. Moreover, this estimator performs poorly when the sample size is not sufficiently large. To address this problem, we propose a new shrinkage LMMSE estimator. The proposed estimator performs efficiently over a wide range of observation dimensions and sample sizes. In contrast to existing methods, the proposed estimator can be applied if M ≥ N. Even if M <; N, the proposed estimator performs more efficiently than existing estimators.

Using large-dimensional random matrix theory (RMT), we conduct mutual information analysis of a multiple-input multiple-output (MIMO) multiple access channel (MAC). Our channel model reflects the characteristics in small-cell networks where antenna correlations, line-of-sight components, and general type of fading distributions have to be included. The mutual information expression can be expressed as functionals of the Stieltjes transform through the so-called Shannon transform. Ideally, if the Stieltjes transform is known in the context of the large-dimensional RMT, then the problem is solved. However, it is difficult to derive the Stieltjes transform of the considered channel models directly, especially when the transmit correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian. To overcome this, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or non-Gaussian independent entries coincide in the large-dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for non-Gaussian MIMO channels from the known results developed for Gaussian MIMO channels. As an application, we determine the capacity-achieving input covariance matrices for the MIMO-MACs and prove that the capacity-achieving input covariance matrices are asymptotically independent of the fading distribution.

In this paper, we investigate the asymptotic mutual information expression and the capacity-achieving input covariance matrices for the coordinated multi-point multiple-input multiple-output (MIMO) antenna channels. In particular, it is considered that the numbers of antennas at the transmitter and receiver approach to infinity with a fixed ratio. Our derivations are based on novel techniques from large dimensional random matrix theory. In contrast to previous studies, we consider a general model in which the correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian distributed. We show that the asymptotic capacity is invariant to all types of fading distribution. As such, the asymptotic mutual information expression is robust and has wide applicability.

A high-accuracy and low-complexity TOA-based algorithm is proposed to estimate mobile station (MS) location. First, a factor-graph-based distributive approach is used, which can work in a multi-state system as well. By effectively exchanging the available soft-information or stochastic property of the variables, this distributive approach can perform almost as well as conventional algorithm while requiring much less computational workload. Second, data screening method was applied to reduce complexity and data discrimination to improve accuracy. Simulation results demonstrate that this approach can maintain good accuracy while achieving low complexity. Since this approach is accurate and easy to implement, it will better satisfy the demands of real applications.

In this paper, we address statistically robust transmit design problems that maximize the average mutual information of amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-hop relay channels having perfect channel state information at the receiver (CSIR) and statistical channel state information at the transmitter (CSIT). In the selected scenario, the source, relay, and destination terminals are equipped with correlated antennas where a direct link between the source and the destination terminals can be found. Moreover, the statistical CSIT consists of the channel means and covariance matrices of various links. The design problem is taken into account for regimes in large systems because there are no closed-form expressions of the average mutual information in the MIMO relay channel. The contribution of this study includes the derivation of asymptotic mutual information expressions for the MIMO relay channel in the large system limit. Additionally, an efficient optimization algorithm based on the asymptotic mutual information is proposed in order to find the asymptotically optimal source covariance matrix and the relay amplifying matrix. Numerical simulation results show that the new approach achieves indistinguishable performance compared to those of the maximization approaches in finite-dimensional systems, even for a small number of antennas at each link. The impact of the CSI of various links on the throughput of the MIMO relay channel is studied using the new approach.

In this paper, we study the capacity-achieving input covariance matrices for the jointly-correlated (or the Weichsel-berger) Rician fading multiple-input multiple-output (MIMO) antenna channel when perfect channel state information (CSI) is known at the receiver while only statistical CSI at the transmitter is available. Unlike the Kronecker model, such jointly-correlated MIMO channel accounts for the correlation coupled between the two ends and has been shown to be most accurate for representing real channels. Our contribution includes the expression for the asymptotic mutual information for the jointly-correlated Rician fading MIMO channel in the large-system regime in which the numbers of antennas at the transmitter and receiver go to infinity with a fixed ratio. Based on this expression, an efficient algorithm is also proposed to obtain the capacity-achieving input covariance matrix. Simulation results demonstrate that even for a moderate number of antennas at each end, the proposed scheme provides undistinguishable results as those obtained by the highly-complex stochastic programming (or Monte-Carlo based) approach.

This paper considers the relay subset selection problem in a two-way amplify-and-forward relay network, where each user is equipped with multiple antennas and each relay is equipped with a single antenna. In a two-way channel, two users exchange data with each other using a relay subset. With the optimal relay subset selection scheme, the system can reduce the hardware burden while preserving the diversity benefit. However, the optimal relay subset selection algorithm requires an exhaustive search of all possible combinations to determine the optimum subset in order to achieve the maximum sum-rate. Thus, this results in high computational complexity. In order to reduce the computational load while still maximizing the achievable rate, a cross-entropy (CE) method is introduced to search for the optimal relay subset. Simulation results indicate that the proposed CE method can guarantee a result that is within 98% of the optimum sum-rate obtained by the exhaustive search method with low computational complexity.

In this paper, we study the transmit precoding problem for multiple-input multiple-output (MIMO) multiple-access (MA) cellular systems employing cooperative base stations (BSs) and amplify-and-forward (AF) relaying where the antennas at the user equipments (UEs), relay terminals (RTs), and BSs are correlated. It is assumed that BSs have perfect channel state information (CSI) while user equipments (UEs) and RTs only have the channel covariance information (CCI). We derive the asymptotic sum-rate expressions for the MIMO-MA cellular systems for any given signaling input distributions in the large-system limit. Using the asymptotic results, we propose an efficient algorithm to determine the asymptotic optimum transmit matrices for the UEs and RTs that can maximize the sum-rate of the MIMO-MA cellular systems if CCI is exploited at the UEs and RTs.

This letter considers selection of the optimal peak reduction tone (PRT) set for the tone reservation (TR) scheme to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. In the TR scheme, PAPR reduction performance achieved by a randomly generated PRT set is superior to that by a consecutive PRT set and that achieved by an interleaved tone set. However, the optimal PRT set requires an exhaustive search of all combinations of possible PRT sets, which is known to be a nondeterministic polynomial-time (NP)-hard and cannot be solved for the practical number of tones. Inspired by the efficiency of the cross-entropy (CE) method for finding near-optimal solutions in huge search spaces, this letter proposes the application of the CE method to search the optimal PRT set. Computer simulation results show that the proposed CE method obtains near-optimal PRT sets and provides better PAPR performance.

This paper considers the use of the sign-selection technique to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. In the sign-selection technique, a set of subcarrier signs is selected to significantly reduce the PAPR statistics for OFDM signals. However, the considerable computational complexity for an exhaustive search over all combinations of 2Ldifferent sign patterns is a potential problem for practical implementation, whereLis the number of subcarriers. To reduce the computational complexity while still improving the PAPR statistics, we introduce the quantum-inspired evolutionary algorithm (QEA), an effective algorithm that solves various combinatorial optimization problems, to determine a good set of subcarrier signs. The computer simulation results show that as compared to the conventional selected mapping (SLM) scheme and the cross-entropy (CE) method, the proposed QEA obtains the desirable PAPR reduction with low computational complexity.

This letter considers the use of the tone injection (TI) scheme to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. TI is a distortionless technique that can reduce PAPR significantly without data rate loss and does not require the extra side information. However, the optimal TI scheme requires an exhaustive search over all combinations of possible permutations of the expanded constellation, which is a potential problem for practical applications. To reduce the computational complexity while still improving PAPR statistics, this letter first formulates the PAPR reduction with TI scheme as a particular combinatorial optimization problem. Next, it proposes the application of the cross-entropy (CE) method to solve the problem. Computer simulation results show that the proposed CE method obtains the desired PAPR reduction with low computational complexity.

Using the replica method, originally developed in statistical physics, we derive the asymptotic mutual information (MI) of a spatially correlated Rician multiple-input multiple-output (MIMO) multiple-access (MA) channel for any given signaling input distributions (not necessarily Gaussian) in the large-system regime where the numbers of antennas at the transmitters and the receiver go to infinity with a constant ratio. In addition to the result that uses joint optimum decoding (JOD) at the central receiver, we also obtain asymptotic characteristics of a more practical system which performs separate detection and decoding (SDD) (i.e., spatial detection followed by a temporal error-correction decoder). Simulation results reveal that the asymptotic results provide promising estimates for the average performance metrics (e.g., the error probability and the MI) even with only a few antenna elements at the transceivers.

This paper considers the problem of target location estimation in a wireless sensor network based on IEEE 802.15.4 radio signals and proposes a novel implementation of the maximum likelihood (ML) location estimator based on the Cross-Entropy (CE) method. In the proposed CE method, the ML criterion is translated into a stochastic approximation problem which can be solved effectively. Simulation results comparing the performance of a ML target estimation scheme employing the conventional Newton method and the conjugate gradient method are presented. The simulation results show that the proposed CE method provides higher location estimation accuracy throughout the sensor field.

This letter considers the use of the partial transmit sequence (PTS) technique to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. The conventional PTS technique can provide good PAPR reduction performance for OFDM signals; however, it requires an exhaustive search over all combinations of allowed phase factors, resulting in high complexity. In order to reduce the complexity while still improving the PAPR statistics of an OFDM signal, a new method using the cross-entropy (CE) method is proposed to reduce both the PAPR and the computational load. In the proposed CE method, we first define a score or fitness function based on the corresponding PAPR reduction performance. The score function is then translated into a stochastic approximation problem which can be solved effectively. The simulation results show that the performance of the proposed CE method provides almost the same PAPR reduction as that of the conventional exhaustive search algorithm while maintaining low complexity.

Inspired by the success of the low-density parity-check (LDPC) codes in the field of error-control coding, in this paper we propose transforming the downlink multiuser multiple-input multiple-output scheduling problem into an LDPC-like problem using the normal graph. Based on the normal graph framework, soft information, which indicates the probability that each user will be scheduled to transmit packets at the access point through a specified angle-frequency sub-channel, is exchanged among the local processors to iteratively optimize the multiuser transmission schedule. Computer simulations show that the proposed algorithm can efficiently schedule simultaneous multiuser transmission which then increases the overall channel utilization and reduces the average packet delay.

This paper presents a low complexity algorithmic framework for finding a broadcasting schedule in a low-altitude satellite system, i.e., the satellite broadcast scheduling (SBS) problem, based on the recent modeling and computational methodology of factor graphs. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, in this paper, we transform the SBS problem into an LDPC-like problem through a factor graph instead of using the conventional neural network approaches to solve the SBS problem. Based on a factor graph framework, the soft-information, describing the probability that each satellite will broadcast information to a terminal at a specific time slot, is exchanged among the local processing in the proposed framework via the sum-product algorithm to iteratively optimize the satellite broadcasting schedule. Numerical results show that the proposed approach not only can obtain optimal solution but also enjoys the low complexity suitable for integral-circuit implementation.

This paper studies the asymptotic performance of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network where spatial correlations at both the transmitters (i.e., mobile stations) and the central receiver (i.e., base station) exist, and more than one multiantenna sets are employed at the receiver to provide macrodiversity. The sense of asymptotic behavior we consider is the large-system limit in which the numbers of antennas at each mobile station and antenna set go to infinity with their ratios fixed. Using the replica method, we derive analytical solutions to the asymptotic spectral efficiency (SE) of the MIMO-MA systems for any given input distributions (not necessarily Gaussian) from the mobile transmitters. Our results can be regarded as the generalization of many previously known results for degenerate cases. Another contribution of this paper is an efficient algorithm to determine the asymptotic optimum transmit-signal covariance matrices that can maximize the SE of the MIMO-MA network within the period in which the spatial channel covariance information (CCI) can be considered static, and assuming that only CCI is available at the transmitters

To extend network coverage and to possibly increase data packet throughput, the future wireless cellular networks could adopt relay nodes for multi-hop data transmission. This letter proposes a novel distributed scheduling algorithm for downlink relay networks. Soft-information indicating the probability of activating each network link is exchanged iteratively among neighboring network nodes to determine an efficient schedule. To ensure collision-free simultaneous data transmissions, collision-avoiding local constraint rules are enforced at each network node. To increase resource utility, the soft-information is weighted according to the urgency of data transmission across each link, which also helps maintain throughput fairness among network users.

In this paper, we are interested in performance analyses of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network where Wyner's linear cellular array model is adopted. Notwithstanding some recent results have shown that, in the Wyner's model, the joint processing of the entire received signal with access point (AP) cooperation can dramatically enhance the system throughput because of the macrodiversity. However, in practice, the channel estimation quality from the providers of the macrodiversity is usually poor. Furthermore, existing literatures do not take channel estimation error into account. The channel estimation error provides us the motive to derive analytical solutions to the large-system throughput (or channel sum-rate) of the MIMO cellular network with imperfect channel estimations. Unlike the most large-system results, our derived results can not only be applied in the scenarios with any given input distributions (not necessarily Gaussian) from the mobile transmitters, but also clarify the performance of the network which was originally involved. Several issues we address including (1) how much of coherence interval should be spent for training and (2) the impact of AP cooperation is highlighted through the analytical results.

In this paper, we analyze the bit-error-rate (BER) performance of the optimum multiuser detection (MUD) with channel mismatch in multicarrier code-division-multiple-access (MC-CDMA) systems. The BER performance of the optimum MUD without channel mismatch in MC-CDMA systems has been recently derived using the replica method. However, it is left unjustified, since the replica method is not a rigorous approach. In addition, it is NP-hard to implement an optimum MUD algorithm. To justify the BER performance and to make the optimum MUD feasible, based on Pearl's belief propagation (BP) scheme, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems. Furthermore, channel mismatch is introduced into the BP-based MUD algorithm to make the scenario general. With channel mismatch, the analytical results of the BP-based MUD algorithm conform perfectly to, and the simulation results of the BP-based MUD algorithm conform very closely to the BER performance of the optimum MUD derived using the replica method, which is a nontrivial extension of the existing replica approach mentioned above. Without channel mismatch, the problem becomes a special case of our contribution.

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications

A low-complexity high-accuracy algorithm is proposed to estimate the location of a target MS based on network-side time-of-arrival (TOA) measurements. Under a factor graph framework, the proposed algorithm first constructs a graphical model for the mobile position location problem by dividing the problem into many mutually-interactive local constraints. Each local constraint is enforced by a separate local processing unit. Efficient exchange of soft-information among local processing units in the mobile switching center (MSC) then iteratively purifies the estimate of the MS location. Numerical results show that the proposed algorithm not only enjoys low complexity, suitable for integrated-circuit implementation, but it is also able to achieve performance very close to the optimum achievable solution accuracy, the maximum likelihood (ML) solution accuracy

Recently, several published papers have been drawing attention to multicarrier code division multiple access (MCCDMA) systems, especially their applications in the field of broad-band wireless communications. In such applications, existing suboptimal multiuser detectors (MUDs) usually do not achieve acceptable performance. Unfortunately, it is NP-hard to implement an optimum MUD algorithm. In this paper, to make the optimum MUD feasible, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems based on Pearl's belief propagation (BP) scheme. The BP-based MUD algorithm can be implemented in a practical time scale. The analytical dynamic biterror-rate (BER) performance of the BP-based iterative detector can be traced accurately and its BER in the stationary state is shown to achieve the performance obtained by the optimum MUD algorithm.

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications.

Evolution of cellular systems in urban environments results in microcell and macrocell overlaying architectures with heavy traffic loading seen around hot spots. To achieve high spectrum efficiency, serious traffic blocking problems usually emerge in such an architecture if the network resource is not well managed. Based on a normal graph framework, we propose a fully-distributed dynamic channel assignment algorithm for hierarchical cellular systems using antenna arrays. Utilizing the available mobile station location information, the proposed algorithm defines soft-information to reflect the local traffic distribution. The soft-information is then iteratively exchanged among mobile stations and base stations to increase system capacity significantly.

Making use of the network side time-of-arrival (TOA) measurements, we propose a low-complexity high-accuracy algorithm to estimate the location of a target mobile station (MS). Under a factor graph framework, the proposed algorithm efficiently exchange soft information among local processing units in the mobile switching center (MSC) to iteratively purify the estimate of the MS location. Numerical results show that the proposed algorithm not only enjoys advantages of low complexity, suitable for integrated-circuit implementation, but it is also able to achieve performance very close to the optimum achievable bound, the maximum likelihood (ML) bound.