In this paper, we investigate the low-complexity distributed combining scheme design for near-field cell-free extremely large-scale multiple-input-multiple-output (CF XL-MIMO) systems. Firstly, we construct the uplink spectral efficiency (SE) performance analysis framework for CF XL-MIMO systems over centralized and distributed processing schemes. Notably, we derive the centralized minimum mean-square error (CMMSE) and local minimum mean-square error (LMMSE) combining schemes over arbitrary channel estimators. Then, focusing on the CMMSE and LMMSE combining schemes, we propose five low-complexity distributed combining schemes based on the matrix approximation methodology or the symmetric successive over relaxation (SSOR) algorithm. More specifically, we propose two matrix approximation methodology-aided combining schemes: Global Statistics & Local Instantaneous information-based MMSE (GSLI-MMSE) and Statistics matrix Inversion-based LMMSE (SI-LMMSE). These two schemes are derived by approximating the global instantaneous information in the CMMSE combining and the local instantaneous information in the LMMSE combining with the global and local statistics information by asymptotic analysis and matrix expectation approximation, respectively. Moreover, by applying the low-complexity SSOR algorithm to iteratively solve the matrix inversion in the LMMSE combining, we derive three distributed SSOR-based LMMSE combining schemes, distinguished from the applied information and initial values.

Movable antenna (MA) technology has recently attracted significant research attention as a promising solution for enhancing wireless network performance. However, conventional MAs can only effectively serve users in close proximity, resulting in restricted coverage. To overcome this limitation, in this paper, we explore a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided MA system. First, we propose a penalty-based block coordinate descent optimization algorithm tailored to the new constraints imposed by BD-RIS-aided MA systems. Specifically, our method decouples the inherently non-convex and coupled antenna distance constraints by introducing auxiliary optimization variables. Subsequently, the resulting problem is efficiently addressed via alternating optimization, with closed-form updates for the auxiliary variables. Furthermore, recognizing the challenges posed by large-scale BD-RIS deployments, which have the potential for serving a substantial number of users, traditional centralized optimization frameworks encounter considerable difficulties, including high computational complexity, excessive communication overheads, as well as limited scalability with increasing system size. To address these limitations, we propose an efficient asynchronous alternating direction method of multipliers (AS-ADMM) scheme aimed at maximizing the sum rate. Our numerical results demonstrate that the BD-RIS-aided MA system achieves superior performance compared to both conventional fixed position antenna and BD-RIS-aided systems. Furthermore, the proposed AS-ADMM framework can achieve a trade-off between performance and computational overhead, highlighting its potential for practical implementation in large-scale wireless communication networks.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is an emerging transceiver technology for enabling next-generation communication systems, due to its potential for substantial enhancement in both the spectral efficiency and spatial resolution. However, the achievable performance limits of various promising XL-MIMO configurations have yet to be fully evaluated, compared, and discussed. In this paper, we develop an effective degrees of freedom (EDoF) performance analysis framework specifically tailored for near-field XL-MIMO systems. We explore five representative distinct XL-MIMO hardware designs, including uniform planar array (UPA)-based with infinitely thin dipoles, two-dimensional (2D) continuous aperture (CAP) plane-based, UPA-based with patch antennas, uniform linear array (ULA)-based, and one-dimensional (1D) CAP line segment-based XL-MIMO systems. Our analysis encompasses two near-field channel models: the scalar and dyadic Green’s function-based channel models. More importantly, when applying the scalar Green’s function-based channel, we derive EDoF expressions in the closed-form, characterizing the impacts of the physical size of the transceiver, the transmitting distance, and the carrier frequency. In our numerical results, we evaluate and compare the EDoF performance across all examined XL-MIMO designs, confirming the accuracy of our proposed closed-form expressions. Furthermore, we observe that with an increasing number of antennas, the EDoF performance for both UPA-based and ULA-based systems approaches that of 2D CAP plane and 1D CAP line segment-based systems, respectively. Moreover, we unveil that the EDoF performance for near-field XL-MIMO systems is predominantly determined by the array aperture size rather than the sheer number of antennas.

In this paper we study the effective degree of freedom (EDoF) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. We consider two XL-MIMO hardware designs, uniform planar array (UPA) based and continuous aperture (CAP) based XL-MIMO, as well as two representative near-field channel models: scalar Green function based and dyadic Green function with triple polarization based models. First, for UPA-based XL-MIMO with a discrete array aperture, we evaluate the EDoF performance by applying discrete channel matrices generated by the scalar or dyadic Green channel model. Then, for CAP-based XLMIMO, a tailored EDoF performance evaluation framework for a two-dimensional (2D) CAP plane based system is constructed by leveraging asymptotic analysis and extending the analysis approaches for a one-dimensional (1D) CAP line segment based system. This framework incorporates the triplepolarized auto-correlation kernel function, which can efficiently capture the impact of multiple polarization on the EDoF performance. Numerical results show that, with an increase in the number of antennas, the UPA-based XL-MIMO system can achieve an EDoF performance close to the EDoF performance for the CAP plane based XL-MIMO system. Moreover, the EDoF performance can be enhanced by the multiple polarization in channels and increased physical size of the transceiver.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has 51.3% reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.

Cell-free massive multiple-input multiple-output (mMIMO) has recently been proposed to improve cell edge performance. However, most prior works consider perfect hardware impairments (HIs), which are difficult to be achieved in practical systems. This paper studies the impact of HI in an uplink cell-free mMIMO system with both multi-antenna access points (APs) and multi-antenna user terminals (UTs) under the Weichselberger channel model.Firstly, we study a two-layer decoding scheme with local minimum mean-squared error or maximum ratio combining at the AP side and with optimal large-scale fading decoding in the central processing unit. We derive novel closed-form SE expressions and prove that the effect of HI can be mitigated in the case of UTs with multiple antennas. However, the achievable SE is constrained by the pilot contamination and pilot overhead. To this end, the superimposed pilot (SP) transmission method is considered in this paper, where all the coherence intervals are used for both pilot and data symbols transmission. Finally, numerical results verify our derived expressions and reveal the relationship between HI and the number of antennas per UT for different pilot schemes. Note that the advantages of SP over regular pilots disappear when the hardware quality decreases with multi-antenna UTs.

In this paper, we investigate a cell-free massive multiple-input multiple-output system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) for the fully centralized processing scheme and large-scale fading decoding (LSFD) scheme. To further improve the SE performance, we design the uplink precoding schemes based on the weighted sum SE maximization. Since the weighted sum SE maximization problem is not jointly over all optimization variables, two efficient uplink precoding schemes based on Iteratively Weighted sum-Minimum Mean Square Error (I-WMMSE) algorithms, which rely on the iterative minimization of weighted Mean Square Error (MSE), are proposed for two processing schemes investigated. Furthermore, with maximum ratio combining applied in the LSFD scheme, we derive novel closed-form achievable SE expressions and optimal precoding schemes. Numerical results validate the proposed results and show that the I-WMMSE precoding schemes can achieve excellent sum SE performance with a large number of UE antennas.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF mMIMO) system with both multi-antenna user equipments (UEs) and access points (APs) over spatially correlated Rayleigh fading channels. In practi-cal CF mMIMO systems, the in-phase and quadrature-phase imbalance (IQI) and low-resolution analog-to-digital converters (ADCs) at the APs are critical for the system performance. Taking these factors into account, the achievable uplink spectral efficiency (SE) is analyzed based on a two-layer decoding scheme. In particular, the maximum ratio (MR) and local minimum mean-square error (L-MMSE) combining are adopted at the APs while the large-scale fading decoding (LSFD) is implemented at the central processing unit (CPU). Furthermore, we derive novel closed-form SE expressions with the MR combining and investigate the SE performance for different combining schemes, quantization bits, and IQI parameters. Numerical results reveal the performance degradations caused by both the low-resolution ADCs and IQI. Additionally, increasing the number of APs is an effective means to promote the system performance.

We consider a practical spatially correlated recon-figurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) system with multi-antenna access points (APs) over spatially correlated Rician fading channels. The minimum mean square error (MMSE) channel estimator is adopted to estimate the aggregated RIS channels. Then, we investigate the uplink spectral efficiency (SE) with the maximum ratio (MR) and the local minimum mean squared error (L-MMSE) combining at the APs and obtain the closed-form expression for characterizing the performance of the former. The accuracy of our derived analytical results has been verified by extensive Monte-Carlo simulations. Our results show that increasing the number of RIS elements is always beneficial, but with diminishing returns when the number of RIS elements is sufficiently large. Furthermore, the effect of the number of AP antennas on system performance is more pronounced under a small number of RIS elements, while the spatial correlation of RIS elements imposes a more severe negative impact on the system performance than that of the AP antennas.

In this paper, we investigate a cell-free massive MIMO system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) based on a two-layer decoding structure with maximum ratio (MR) or local minimum mean-square error (MMSE) combining applied in the first layer and optimal large-scale fading decoding method implemented in the second layer, respectively. To maximize the weighted sum SE, an uplink precoding structure based on an Iteratively Weighted sum-MMSE (I-WMMSE) algorithm using only channel statistics is proposed. Furthermore, with MR combining applied in the first layer, we derive novel achievable SE expressions and optimal precoding structures in closed-form. Numerical results validate our proposed results and show that the I-WMMSE precoding can achieve excellent sum SE performance.

In this work, we investigate the transmission and offloading strategy in the nonorthogonal multiple access (NOMA)-enabled fog computing system for the Internet of Things (IoT). We aim to minimize the total energy consumption of the IoT system while satisfying the latency requirements. Due to the energy minimization problem is a mixed-integer nonlinear programming, we decompose the problem into two subproblems for different optimizing variables, i.e., fog node selection and resource allocation subproblems, and propose a multinode collaboration transmission and computation (MCTC) algorithm. Specifically, the fog node selection subproblem can be transformed into the assignment problem, which is constructed as a bipartite graph to obtain the node selection strategy. For the resource allocation subproblem, we propose an iterative algorithm to obtain the offloading workload, duration allocation, and computation resource. Simulation results are provided, which demonstrate that the proposed algorithm outperforms the other strategies by 56.88% at least.

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance.

Mobile computation offloading (MCO) is an emerging technology to offload the resource-intensive computations from smart mobile devices (SMDs) to nearby resource-rich devices (i.e., cloudlets) via wireless access. However, the link duration between a SMD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by a SMD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a semi-Markov decision process (SMDP)-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the SMDs in vehicles, and an application generated by a SMD includes a series of tasks that have dependency among each other. In this paper, we adopt a semi-Markov decision process (SMDP) framework to formulate the bus-based cooperation computing problem as a delay-constrained shortest path problem on a state transition graph. The value iteration algorithm (VIA) is used to find the efficient solution to the bus-based cooperation computing problem. Experimental results show that the proposed SMDP-based cloudlet cooperation strategy can improve the performance of computation on the SMD in the cost (i.e., energy consumption and application completion time) and the offloading rate.

Mobile computation offloading is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices (MDs) to resource-rich devices (such as a cloud server) via wireless access. Accessing to remote cloud server usually introduces a long delay to first deliver parameters to the server and then retrieve the results back. For applications that are time sensitive, offloading to nearby cloudlets is preferred. However, the link duration between an MD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by an MD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a bus-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the MDs in vehicles, and an application generated by an MD includes a series of tasks that have dependency among each other. The proposed bus-based cloudlet cooperation strategy (BCCS) finds the optimal set of tasks to be offloaded to each cloudlet. Experimental results show that the proposed BCCS strategy can reduce both the energy consumption of the MDs and completion time of the applications.