Backscatter communication (BackCom) has gained increasing attention due to its low power consumption and low cost. However, its performance is severely degraded by the near-far effect due to the limited backscattering power at the tag side. In this paper, we consider two effective methods to solve the above problem by using the reconfigurable intelligent surface (RIS) and the relay. The objective is to maximize the received signal-to-noise ratio (SNR) subject to the transmit and circuit power control. To gain insight into both schemes, we derive a closed-form expression for the coverage radius of RIS-aided BackCom. Furthermore, considering the relay as a competing technology to the RIS, we compare their performance in terms of transmission performance, required transmit power, and quantify the number of reflecting elements needed for the RIS to outperform the relay. Simulation results demonstrate the RIS-aided BackCom with a sufficiently large number of elements achieves a superior performance, offers greater deployment flexibility, and is suitable for high self-interference levels.

Symbiotic radio (SR) is a promising technology designed to meet the increasing demand for spectrum-efficient communication. However, the small size of backscatter devices (BDs), which are typically equipped with a single antenna, poses challenges in achieving sufficient diversity or spatial multiplexing, thereby hindering the advancement of SR. To address this issue, we introduce fluid antennas (FAs) into SR, enabling devices to dynamically adjust their positions to create a favorable wireless environment and overcome spatial constraints, thereby achieving significant diversity gains. In this paper, we investigate the uplink performance of FA-assisted SR (FA-SR). First, we propose a novel collaborative cancellation channel estimation scheme based on least squares regression (CC-LSR) for scenarios with imperfect channel state information (CSI). We then derive tight lower bound expressions for the channel capacity under both perfect and imperfect CSI cases and formulate the corresponding weighted sum channel capacity (WSCC) optimization problems. The positions of the FAs and the combining vectors are jointly optimized to maximize the lower bound of the WSCC. To solve these problems, we develop joint optimization methods for both perfect and imperfect CSI scenarios using chaotic sequence-based adaptive particle swarm optimization (CSA-PSO). Nevertheless, the high computational complexity of joint optimization poses challenges for practical implementation. To this end, we propose a progressive optimization framework (POF) tailored to both perfect and imperfect CSI scenarios, in which the original problem is divided into three subproblems that are progressively solved to find locally optimal solutions. Numerical results demonstrate that POF significantly reduces computational complexity with minimal performance loss compared to joint optimization methods, particularly under imperfect CSI conditions.

In strong line-of-sight (LoS) scenarios, the lack of scattering paths leads to significant rank deficiency in the channel, thereby limiting the spatial multiplexing capabilities of multiple-input multiple-output (MIMO) systems. To address this inherent deficiency, reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution for enhancing the channel scattering environment in the context of sixth-generation (6G) networks. However, traditional passive RISs often require a large number of elements to counteract multiplicative fading. In contrast, active RISs (ARISs) can greatly reduce the reflecting elements requirement and effectively overcome the multiplicative fading. This paper investigates using multiple ARISs to reshape the wireless channel and formulates the effective rank (ER) maximization problem under two ARIS phase-shift models. We propose a multi-phase particle swarm optimization (MPSO) scheme to optimize the phase shifts of the ARISs under a continuous phase-shift model. However, the continuous phase-shift model entails substantial hardware implementation costs. To solve this, we introduce the multi-phase maximum cross-swapping algorithm (MMCA) under a discrete phase-shift model. Furthermore, we explore the impact of ARIS deployment positions on the channel ER and employ the sparrow search algorithm (SSA) to adjust the ARIS positions. The SSA-MPSO scheme is then proposed, which alternately optimizes the deployment positions and phase shifts of multiple ARISs, achieving an ER close to the theoretical upper bound. Simulation results demonstrate that the proposed scheme significantly outperforms baseline schemes, effectively enhancing the spectral efficiency.

Low Earth Orbit (LEO) satellites are playing an important role in earth observation, providing valuable images for training machine learning (ML) models used in tasks such as environmental monitoring and pattern recognition. However, due to unstable communication links and limited downlink bandwidth, it is economically impractical to transmit all raw images to ground stations (GSs) for model training. Federated learning (FL), a privacy-preserving distributed machine learning method, can reduce the communication overhead by exchanging model parameters. Generally, FL needs a fixed central server to aggregate the global model, which is challenging in LEO satellite networks, given the dynamic nature of satellite orbits. To overcome this, we propose a decentralized federated learning (DFL) framework that enables efficient model aggregation through satellite collaboration. Specifically, the proposed framework, named FedHRA, is based on model-agnostic meta-learning (MAML), which jointly optimizes hyperparameters and resource allocation to mitigate straggler effect and address statistical heterogeneity. Extensive numerical results on MNIST and CIFAR-10 datasets demonstrate that FedHRA achieves shorter learning time and higher model accuracy compared to the benchmark frameworks.

Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.

Cell-free massive multiple-input multiple-output (CF massive MIMO) is regarded as a promising technology for next-generation wireless communication systems. However, relying on a single central processing unit (CPU) in CF massive MIMO systems is not scalable in practical networks, requiring the introduction of multiple CPUs for more efficient and feasible transmission. In this paper, we investigate a CF massive MIMO system with multiple CPUs. To obtain flexible and cost-efficient deployment, we propose to use wireless x-haul links instead of wired ones. More specifically, we assume that both the fronthaul links from the APs to the corresponding CPU and the backhaul links between CPUs operate under millimeter wave (mmWave) networks. Taking into account a tradeoff between the degree of centralized coordination and the signal overhead on the backhaul links, we consider four levels of multiple CPUs cooperation schemes from fully centralized to fully distributed. In addition, we propose a binary search method to allocate the backhaul capacities for maximizing the sum spectral efficiency (SE). Simulation results show that mmWave backhaul amplifies the compression noise introduced by mmWave fronthaul, leading to a more pronounced impact on the SE of systems. In this case, the centralized processing scheme can generate more compression noise due to the larger data overhead on the backhaul link, making the distributed processing scheme a superior processing scheme, especially when dealing with a large number of APs or significant distances between CPUs.

In Federated Learning (FL), with parameter aggregated by a central node, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the single point of failure within the FL framework, recent studies have introduced Decentralized Federated Learning (DFL) as a viable alternative. Considering the device heterogeneity, and energy cost associated with parameter aggregation, in this paper, the problem on how to efficiently leverage the limited resources available to enhance the model performance is investigated. Specifically, we formulate a problem that minimizes the loss function of DFL while considering energy and latency constraints. The proposed solution involves optimizing the number of local training rounds across diverse devices with varying resource budgets. To make this problem tractable, we first analyze the convergence of DFL with edge devices with different rounds of local training. The derived convergence bound reveals the impact of the rounds of local training on the model performance. Then, based on the derived bound, the closed-form solutions of rounds of local training in different devices are obtained. Meanwhile, since the solutions require the energy cost of aggregation as low as possible, we modify different graph-based aggregation schemes to solve this energy consumption minimization problem, which can be applied to different communication scenarios. Finally, a DFL framework which jointly considers the optimized rounds of local training and the energy-saving aggregation scheme is proposed. Simulation results show that, the proposed algorithm achieves a better performance than the conventional schemes with fixed rounds of local training, and consumes less energy than other traditional aggregation schemes.

Cell-free massive multiple-input multiple-output (CF massive MIMO) is regarded as a promising technology for next-generation wireless communication systems. However, conventional CF massive MIMO systems typically employ fixed-position antennas (FPAs) at access points (APs), which limits the exploitation of spatial degrees of freedom (DoFs) for antenna position optimization. To address this issue, we propose the use of fluid antennas (FAs) in place of FPAs, enabling more DoFs at APs and leading to a novel FA-aided CF massive MIMO (FA-CF) architecture. In this paper, we investigate the uplink spectral efficiency (SE) of FA-CF systems with imperfect channel state information (CSI). We design a minimum mean-square error (MMSE)-based channel estimation scheme to estimate the aggregated channels between APs and user equipments (UEs). We further derive achievable SE expressions for both centralized and distributed processing schemes, including fully centralized processing (FCP), large-scale fading decoding (LSFD), and equal-gain decoding processing (EGDP). Moreover, we formulate a mean-square error (MSE) minimization problem based on the signal transmission model. To solve this problem, we develop an efficient algorithm that combines orthogonal matching pursuit (OMP) with binary search to jointly optimize FA positions and the combining matrix. In addition, we propose a protective weak-ordering (PWO) strategy to enhance the SE of the FCP scheme. Numerical results demonstrate that FA-CF significantly outperforms conventional CF systems in terms of SE, even with a limited number of antennas, and maintains strong robustness under imperfect CSI or heavy UE loads by adaptively adjusting antenna positions. These results highlight FA-CF as a promising architecture offering enhanced SE and robustness for future wireless systems, particularly in scenarios where large-scale AP deployment is infeasible or cost-constrained.

In strong line-of-sight (LoS) communication systems, the lack of scattering paths leads to significant rank deficiency in the channel matrix, thereby limiting the spatial multiplexing gain of multiple-input multiple-output (MIMO) systems. To address this inherent deficiency, this paper utilizes multiple active reconfigurable intelligent surfaces (ARISs) to reshape the wireless channel and solve the effective rank (ER) maximization problem. We propose a multi-phase particle swarm optimization (MPSO) scheme to optimize the phase shifts of the ARISs. Furthermore, we explore the impact of ARIS deployment positions on the channel ER and employ the sparrow search algorithm (SSA) to adjust the ARIS positions. Then we propose the SSA-MPSO scheme, which alternately optimizes the deployment positions and phase shifts of multiple ARISs, achieving an ER close to the theoretical upper bound. Simulation results demonstrate that the proposed scheme significantly outperforms baseline schemes, effectively enhancing the spatial multiplexing gain under imperfect channel state information (CSI).

This paper investigates the covert communication (CC) performance of a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) and rate splitting (RS) systems operating over Rician fading channels. Alice applies RS and NOMA to the downlink transmission of two legitimate users aided by the STAR-RIS in the presence of two non-colluding illegal users. Specifically, closed-form expressions for detection error probability, optimal detection threshold, minimum detection error probability (MDEP) of the warden, and the covert rate of the NOMA user pair are derived. The accuracy of the derived results is verified through Monte Carlo simulations. The results demonstrate that the MDEP depends only on the power allocation factor of the covert users and is independent of the transmit power or STAR-RIS deployment distance. Furthermore, the RS-NOMA system exhibits superior CC performance compared to the conventional NOMA system.

Cell-free massive multiple-input multiple-output symbiotic radio (CF-SR) has recently been introduced as a promising solution for the Internet of Things (IoT), offering cost advantages and more uniform coverage performance for user devices. However, most previous studies assume perfect hardware, which is impractical in IoT systems. In this article, we investigate the uplink performance of CF-SR systems in the presence of hardware impairments (HWIs). We adopt two novel schemes for hybrid combining, namely hybrid maximum ratio (HMR) and hybrid local minimum mean square error (HL-MMSE), both of which effectively enhance the spectral efficiency (SE) of the backscattering link. We derive closed-form expressions for the achievable SE of both conventional MR and HMR combining schemes, taking into account the imperfect channel state information (CSI) and HWIs. The simulation results that both the direct and backscattering links are primarily limited by HWIs including multiplicative and additive distortions from the device side. We apply the differential evolution (DE) algorithm for power control to maximize the minimum SE, and the results show that the DE algorithm improves the minimum SE by approximately 52%, avoiding further degradation of the SE of the weakest device due to the impact of HWIs.

Reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) technology has tremendous potential to revolutionize wireless communications by dynamically adapting wireless channels to boost average rate and energy efficiency (EE) of Internet of Things (IoT) networks for meeting the specifications of ultra-reliable and low-latency communications (URLLC). In this paper, we study the downlink harvested energy (HE), uplink rate, and total EE of the wireless-powered RIS-aided CF-mMIMO communication system with hardware impairments under finite blocklength. IoT devices harvest energy from the energy signals transmitted from access points (APs) during the downlink and use it for the uplink pilot and data transmission. Specifically, based on the unique characteristics of the channel fading model and the RIS deployment location, we propose a novel RIS phase shift design according to the line-of-sight (LoS) components of channels. Furthermore, we derive the average HE and uplink rate in closed form with a two-layer decoding method. We also validate the effectiveness of the proposed RIS phase shift design and the derived closed-form expressions by Monte Carlo simulations. Moreover, it is interesting to find that local minimum mean squared error (L-MMSE) combining is recommended to meet the requirements of URLLC, including communication reliability and delay. More notably, the numerical results show that the RIS-aided system with impaired hardware exhibits even superior performance, compared to the system with ideal hardware and more APs but lacking the assistance of RISs.

Due to the numerous advantages of dual-band infrared radiation (DBIR) attitude measurement (AM) technology, it has garnered significant attention from industry and academia. However, geometric errors caused by sensor measurement noise, assembly positions, motor interference and other sensor-related commonalities, along with random errors induced by the sensitivity of DBIR characteristics to infrared radiation interference, collectively undermine the reliability of the estimated attitude information. To address this issue, the bidirectional gated recurrent unit (Bi-GRU) and Transformer were combined to assist extended kalman filter (EKF) for DBIR attitude estimation (CBGTE). The core concept involves two aspects: 1) utilizing EKF to mitigate geometric errors during the process of DBIR AM, and 2) combining Bi-GRU and Transformer to aid EKF in compensating for random errors in the measurement process. A semi-physical experimental platform is established to validate the performance of CBGTE. Through experimental validation with real-world data, the proposed CBGTE algorithm has demonstrated significant improvements in accuracy when compared with several state-of-the-art algorithms, achieving roll angle error of ±0.4° and pitch angle error of ±0.2°.

This paper proposes a blockchain-integrated federated learning (FL) framework tailored for secure, efficient, and energy-aware model training in edge computing environments. The framework follows an offload-train-aggregate paradigm where edge devices transmit local datasets to proximate servers for localized model updates. A reputation-driven RAFT consensus protocol is incorporated to achieve reliable, low-latency, and lightweight blockchain coordination while preserving privacy and accountability. To overcome the inherent mixed-integer nonlinear programming (MINLP) complexity, we develop a two-stage cross-layer optimization strategy. In the first stage, an alternating direction method of multipliers (ADMM)-based feedback control scheme jointly allocates bandwidth and computation resources under energy and delay constraints. In the second stage, server selection and sub-band assignment are modeled as a bipartite matching problem and solved via the Hungarian algorithm, guided by a convergence-aware performance bound. Extensive simulations demonstrate that our framework significantly improves learning accuracy, uplink throughput, and energy efficiency over state-of-the-art FL baselines. It also exhibits strong robustness to network fragmentation and resource heterogeneity, making it well suited for practical edge environments.

Cell-free symbiotic radio (CF-SR) is a promising technology to meet the demands of good quality-of-service and spectrum-efficient communications. However, the introduction of SR brings additional interference terms, which can seriously degrade the performance of the CF-SR systems. To suppress the interference, we adopt a rate-splitting (RS) transmission scheme to CF-SR. In this paper, we derive downlink spectral efficiency (SE) expressions of the CF-SR system with RS. Furthermore, in a conventional two-phase (TP) channel estimation scheme, the direct link causes heavy interference to the backscatter link, consequently diminishing the accuracy of the backscatter-link channel estimation. To this end, we propose a collaborative cancellation (CC) channel estimation scheme, which can eliminate the interference from the direct link and thus improve the accuracy of the backscatter-link channel estimation. Moreover, we derive the novel closed-form SE expressions under the CC channel estimation scheme using maximum ratio (MR) precoding. Simulation results show that the normalized mean square error (NMSE) of the CC channel estimation is consistently better than the one of the TP channel estimation, both on the direct and backscatter links. Furthermore, the advantages of the CC channel estimation scheme on the backscatter link can be further amplified in scenarios with a sufficient number of pilots. In addition, simulation results demonstrate that both the CC channel estimation scheme and the RS transmission scheme can provide significant improvements.

Reconfigurable intelligent surface (RIS) has the potential to revolutionize wireless communications by dynamically controlling wireless channels to boost spectral efficiency (SE) and energy efficiency (EE), towards meeting the advanced specifications of Internet of Things (IoT) networks. In this context, we study the downlink harvested energy (HE), uplink SE and total EE of the RIS-aided cell-free massive multiple-input multiple-output (CF-mMIMO) system with wireless power transfer (WPT) technology. IoT devices harvest energy from the energy signals transmitted from access points (APs) during the downlink and use it for the uplink pilot and data transmission. Based on the unique characteristics of the channel fading model and the RIS deployment location, we put forward a novel RIS phase shift design scheme according to the line-of-sight (LoS) components of channels and verify its effectiveness. Furthermore, we derive the average HE and uplink SE in closed form with a two-layer decoding method (i.e., the maximal ratio combining (MRC) at APs is called first-layer decoding and the large-scale fading decoding (LSFD) at CPU is called second-layer decoding.) under the assumptions of both perfect and imperfect statistical channel state information (CSI). The results verify the derived closed-form expressions by Monte-Carlo simulations. Increasing the number of RIS elements further improves the uplink SE and total EE with the two-layer decoding. Since the statistical CSI is unknown in practical scenarios, we propose an acquisition method for the statistical CSI applicable to this system. Simulation results validate the efficiency of the proposed statistical CSI acquisition method. Furthermore, it is interesting to find that better statistical CSI estimation can be achieved with more coherent blocks of pilot.

The increasing connectivity and automation in the Internet of vehicles (IoV) have significantly heightened the risk of network attacks, making intrusion detection systems (IDS) a crucial component of security measures in intelligent transportation systems (ITS). To address this challenge, we propose an advanced intrusion detection method integrating knowledge graph (KG) and artificial intelligence (AI) techniques, termed IDS-IKGAI, to enhance the security of IoV infrastructures. In our proposed scheme, we first preprocess an intrusion detection dataset specific to IoV, i.e., feature selection and extraction, that can be represented as triples using the resource description framework (RDF). These RDF triples are used to construct a knowledge graph, capturing the semantic relationships among the features. Next, we map the knowledge graph to a vector space, to build a labeled dataset for machine learning. To train and predict potential intrusions, the random forest (RF) and light gradient boosting machine learning (LightGBM) algorithms are investigated. Experimental evaluations demonstrate the effectiveness of our proposed scheme, with around F1 scores of 99.99% for RF and 99.93% for LightGBM, outperforming conventional benchmark models.

Cell-free massive multiple-input multiple-output symbiotic radio (CF-mMIMO-SR) is a promising technology to address the requirements of high-rate and spectrum-efficient communication for the Internet of Things (IoT). However, in the conventional CF-mMIMO-SR system aided by backscatter devices (BDs), the backscatter link is impacted by double fading without any supplementary compensation, resulting in significantly low spectral efficiency (SE) on the backscatter link. To address this issue, we propose the usage of reconfigurable intelligent surfaces (RISs) instead of BD for symbol-level reflection on the backscatter link, leading to a novel RIS-aided CF-mMIMO-SR (RIS-CF-SR) system. In this paper, we conduct a comprehensive analysis of the RIS-CF-SR system considering different levels of cooperation among the access points (APs). Specifically, we analyze the uplink SEs of four different implementations with arbitrary linear processing on both the direct and backscatter links. Moreover, we investigate different signal cancellation schemes based on full or local channel state information (CSI) to improve the SE of the backscatter link. Through the simulation results, we find that RISs can significantly improve the SE of the backscatter link due to the large number of reflection elements, whereas additional appropriate signal processing schemes are required for the direct link. More specifically, from Level 1 to Level 3, RIS-CF-SR does not have significant advantage in SE over BD-CF-SR on the direct link. At Level 4, RIS-CF-SR can outperform BD-CF-SR on the direct link with the MMSE combining scheme.

Reconfigurable intelligent surface (RIS) is a cost-effective component to enhance wireless energy harvest and spectral efficiency (SE) of wireless communications systems. In the paper, we investigate the performance of a RISs-aided cell-free massive multiple-input multiple-output (CF-mMIMO) system over Rician fading channels in conjunction with wireless power transfer (WPT). User equipments (UEs) harvest energy from the energy signal transmitted from access points (APs) with the help of RISs during the downlink and use it for the uplink pilot and data transmission. Specifically, we derive a closed-form expression for the uplink SE when using a two-layer decoding method and verify the accuracy of the derived results by Monte Carlo simulation. The results show that increasing the number of APs or the number of RIS elements is always beneficial for the system SE improvement. Furthermore, increasing the number of RIS elements can lead to a reduction in the required number of APs without a decrease in the average SE, which means that significant hardware and energy costs can be greatly reduced.

This article considers the uplink of a cell-free massive multiple-input–multiple-output (MIMO) symbiotic radio (CF-mMIMO-SR) system. Conventional combining schemes cannot be used directly to detect the signal of the direct link due to its heavy suppression for the backscatter link. To this end, we propose two hybrid combining schemes, including the hybrid maximum ratio (MR) and hybrid local minimum mean square error (L-MMSE) combining schemes, which take into account the superposition of the two local channel estimation vectors at the access points (APs). When the number of APs goes to infinite, the asymptotic spectral efficiency (SE) of CF-mMIMO-SR with different combining schemes is analyzed. We prove that the conventional combining schemes tend to cause the effective signal of the backscatter link to disappear while the hybrid combining schemes can obtain good performance on the backscatter link. Meanwhile, the performance gap between the two on the direct link is small. In addition, we derive closed-form expressions with the conventional MR combining and hybrid MR combining schemes over independent Rayleigh fading channels. Moreover, we derive the achievable uplink SE expressions with an effective signal-to-interference-and-noise ratio (SINR) for a finite number of antennas. Simulation results verify our theoretical analysis and demonstrate that hybrid combining schemes perform much better on the backscatter link than conventional combining schemes. Specifically, compared to the hybrid MR scheme, the hybrid L-MMSE scheme offers a huge improvement in 95% likely SE, and has negligible performance loss on the direct link.

This letter investigates secrecy performance for cognitive transmissions, where a secondary user (SU) shares same spectrum with a primary user (PU) simultaneously ensuring the Quality of Service (QoS) of primary transmissions. Additionally, an eavesdropper (Eve) overhears cognitive transmissions from SU to base station (BS). To against eavesdropping attacks, a novel artificial noise-aided rate splitting (ANRS) scheme is proposed, where PU emits artificial noise to confuse Eve and SU adopts rate splitting (RS). The numerical results of secrecy outage probability indicates that the ANRS scheme achieves better secrecy performance than that of AN without RS (ANWRS) and of RS without AN (RSWAN) schemes.

This paper investigates the uplink energy efficiency (EE) of a wireless-powered cell-free massive multiple-input multiple-output (mMIMO) system with hardware impairments, considering Rician fading and maximum ratio processing, anchored in linear minimum mean-squared error (LMMSE) channel estimation. The transceivers of access points (APs) and user equipments (UEs) are non-ideal with hardware impairments. The closed-form expressions of the total uplink (UL) EE and the average harvested energy (HE) are derived by employing a non-linear energy harvesting model and coherent transmission schemes. An optimization problem is formulated to maximize the total uplink EE, in which power control coefficients for APs and UEs, and the large-scale fading decoding vectors are considered. An alternating algorithm based on successive convex approximation (SCA) is proposed to tackle the complexity issue of the non-convex optimization problem. The numerical results show that the proposed algorithm can significantly enhance the total uplink EE comparing with the equal power allocation strategies. Moreover, it is revealed that the hardware impairments on the UEs can be the primary limitation to the total uplink EE in comparison with those on the APs, however, the impact can be effectively mitigated by the proposed algorithm.

To leverage massive distributed data and computation resources in the Internet-of-Things (IoT) networks, federated learning (FL) is considered to be a promising technique with benefits of improved data privacy and communication efficiency. Meanwhile, cell-free massive multiple-input–multiple-output (cell-free massive MIMO) is a promising technology to enable the IoT networks to support FL. By deploying the access points (APs) closer to the IoT devices, path loss attenuation can be reduced. However, the performance of FL is still constrained by the limited power resources of IoT devices. To address this issue, we design an energy-efficient FL scheme over cell-free IoT networks by formulating an optimization problem to minimize the total energy consumption of the IoT devices participating in the FL process. To solve the intractable problem in hand, by exploiting its unique structure, we decompose it into three subproblems that facilitate the development of the proposed scheme. First, we derive the optimal central processing unit (CPU) operating frequency for IoT devices. Then, we design an optimal power allocation scheme to mitigate the straggler effect. Next, a nonlinear programming method is adopted to obtain a suboptimal solution for the reformulated problem. Finally, a three-stage algorithm is proposed for energy consumption minimization by considering these subproblems. Simulation results demonstrate the close-to-optimal performance of the proposed algorithm for energy savings compared with three baseline algorithms and the capability to support large numbers of IoT device access by mitigating the straggler effect.

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.

Abstract Intelligent reflecting surface (IRS) is regarded as a promising emerging technology, which has shown enormous potentials for performance enhancement. This paper investigates the multiple IRS‐aided dual‐hop DF relaying system for both single‐user and multi‐user scenarios over Nakagami‐ m fading. Based on a scenario with non‐orthogonal multiple access users, tight upper bounds for the ergodic capacity in perfect channel state information(pCSI) scenario are first derived, and then approximate expressions are obtained in imperfect CSI (ipCSI) mode. Moreover, the performance of multiple IRS‐aided DF relaying system are compared with multi‐IRSs‐only system and DF relaying‐only system. Finally, all the analytical results are verified using Monte Carlo simulations.

This article investigates the performance of nonorthogonal multiple access (NOMA)-based cell-free massive multiple-input–multiple-output (mMIMO) for the Internet of Things (IoT) considering spatially correlated Rician fading channels. The exact closed form of downlink spectral efficiency (SE) and energy efficiency expressions is derived with three estimators and the maximum ratio transmission by taking the impacts of imperfect successive interference cancellation and pilot contamination into account. Subsequently, the performance of a local-MMSE precoder with the three aforementioned estimators is analyzed. Then, a large-scale fading-based user pairing scheme is proposed to further analyze the system SE. Besides, we formulate the optimum power control design as a max–min problem and a computational efficient suboptimal algorithm is proposed based on the successive convex approximation. Furthermore, our results reveal that the magnitude of the spatial correlation negligibly effects the SE in spatially correlated Rician fading channels. Then, numerical results confirm the positive effect of the proposed power control scheme. Also, our results further illustrate that NOMA-based cell-free mMIMO for IoT provides significant performance gain compared with its counterpart deploying conventional orthogonal multiple-access schemes.

Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently. We study the physical layer security of the downlink RIS-aided transmission framework for randomly located users in the presence of a multi-antenna eavesdropper. To show the advantages of RIS-aided networks, we consider two practical scenarios: Communication with and without RIS. In both cases, we apply the stochastic geometry theory to derive exact probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-interference-plus-noise ratio. Furthermore, the obtained PDF and CDF are exploited to evaluate important security performance of wireless communication including the secrecy outage probability, the probability of nonzero secrecy capacity, and the average secrecy rate. Monte-Carlo simulations are subsequently conducted to validate the accuracy of our analytical results. Compared with traditional MIMO systems, the RIS-aided system offers better performance in terms of physical layer security. In particular, the security performance is improved significantly by increasing the number of reflecting elements equipped in a RIS. However, adopting RIS equipped with a small number of reflecting elements cannot improve the system performance when the path loss of NLoS is small.

Cell‐free massive distributed antenna system (CF‐MDAS) can further reduce the access distance between mobile stations (MSs) and remote access points (RAPs), which brings a lower propagation loss and higher multiplexing gain. However, the interference caused by the overlapping coverage areas of distributed RAPs will severely degrade the system performance in terms of the sum‐rate. Since that clustering RAPs can mitigate the interference, in this paper, we investigate a novel clustering algorithm for a downlink CF‐MDAS with the limited‐capacity backhaul. To reduce the backhaul burden and mitigate interference effectively, a semidynamic bidirectional clustering algorithm based on the long‐term channel state information (CSI) is proposed, which has a low computational complexity. Simulation results show that the proposed algorithm can efficiently achieve a higher sum‐rate than that of the static clustering one, which is close to the curve obtained by dynamic clustering algorithm using the short‐term CSI. Furthermore, the proposed algorithm always reveals a significant performance gain regardless of the size of the networks.

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes.

In this paper, we consider the uplink of a dense small cell network (SCN) using pilot reuse in channel training and maximum ratio combining (MRC) for data detection. Taking into account imperfect channel state information (CSI) caused by pilot contamination, we derive exact closed-form expressions of the per-user achievable ergodic rate with arbitrary pilot reuse factors. After that, we first reveal that the user terminals, which are geographically separated with large distance between each other, can reuse pilot, suffering only low pilot contamination. Based on this insight, we further propose a low-complexity pilot reuse algorithm based on the minimum sum of estimation error criterion. Simulation results verify our theoretical analysis and demonstrate that the proposed pilot reuse algorithm is very effective in suppressing pilot contamination in SCN.

This study investigates the scheduling problem for a single‐cell downlink distributed antenna systems (DASs) with a massive number of remote access units (RAUs). To reduce signalling overhead under limited backhaul capacity, the authors make use of local long‐term channel state information (CSI) in coordinated scheduling design. They first derive the ergodic rate expressions for both the single RAU transmission and the cooperative RAU transmission modes as functions of long‐term CSI. Then greedy scheduling algorithms (GSAs) aiming for the maximum ergodic sum rate for the massive DAS using long‐term CSI are proposed. To mitigate the intra‐cell interference, a two‐stage GSA with hybrid transmission mode is devised. Asymptotic analysis reveals that as the number of RAUs goes to infinity, intra‐cell interference can be effectively mitigated. Simulation results verify the analysis and demonstrate that the two‐stage GSA exhibits a higher ergodic sum‐rate.

In this paper, the turbo minimum mean square error- parallel interference cancelation (Turbo MMSE-PIC) receiver is incorporated with link adaptation schemes to achieve promising performance gains for uplink MIMO transmission, in parallel, adapt the link in the actual channel conditions with assist from performance prediction technology. We first predict the turbo receiver's performance as a basic metric for link adaptation scheme. Then, a new strategy of selecting the precoding matrices, the number of spatially multiplexed layers as well as modulation coding schemes are proposed, resulting in the proposed three-steps selection. In this method, the number of layers and modulation coding schemes to be feedback is initialized and used to set a shortened range of them. Then the number of layers is updated to take full advantage of iterative processing, instead of the initial value in the conventional strategy. In so doing, the benefit of turbo receivers is efficiently claimed while low computational complexity can be achieved.

This paper investigates the scheduling problem for a single-cell distributed multiple-input multiple-output (d-MIMO) downlink system with a massive number of remote access units (RAUs), N. We first derive the ergodic rate expressions for both the single RAU transmission (SRT) and the cooperative RAU transmission (CRT) modes as functions of long-term channel state information (CSI). Then, greedy scheduling algorithms aiming for maximizing the ergodic sum rate for the massive d-MIMO system using local long-term CSI are proposed. To mitigate intra-cell interference, a two-stage greedy scheduling algorithm (GSA) is developed to further improve the ergodic sum rate. Asymptotic analysis reveals that with infinite N intra-cell interference can be efficiently mitigated. Simulation results verify the derived expressions and demonstrate that the two-stage GSA exhibits a higher ergodic sum rate.

In this paper, we study adaptive transmission mode switching between statistical and instantaneous channel state information (CSI) aided single-user (SU) and multiuser (MU) precoding for a two-user downlink system, where two transmit antennas are equipped at the base station and each mobile user has one receive antenna. In the case where only statistical CSI (SCSI) is available at the transmitter, a statistical-eigenmode space-division multiple-access (SE-SDMA) scheme is proposed by maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio. An exact analytical expression of the ergodic achievable rate is derived for the proposed SE-SDMA and compared with SU schemes such as SE transmission (SET) and instantaneous CSI (ICSI)-aided beamforming (BF), as well as the MU schemes such as ICSI-aided zero-forcing BF (ZFBF). Assuming the ICSI obtained at the transmitter is imperfect, the operating regions of these schemes are determined for different signal-to-noise ratio regions, channel correlation levels and ICSI inaccuracy levels.

In this paper, we focus on the low complexity algorithm of turbo receivers over double selective channels for the Lone Term Evolution (LTE) single user uplink. Based on the analysis of the physical meaning of the frequency channel matrix, we propose a low complexity algorithm which is significant for the existent frequency LDL turbo receivers. To further reduce the receiver complexity, based on the analysis of the equivalent noise of the conventional turbo equalizer over fast fading channels, a new low complexity sub-block orthogonal segmental turbo receiver is also presented. Through Monte Carlo simulation, we confirm that with lower complexity the performance of the proposed turbo receiver is better than the existent frequency LDL turbo receivers.

This paper proposes limited feedback spatial division multiple access (SDMA) schemes for transmit correlated channels by using statistical channel state information (SCSI) and instantaneous channel state information (ICSI). Different from conventional codebook-based MU-MIMO scheme, the proposed multi-beam selection with single channel quality indicator (CQI) feedback (MBS-SCF) scheme determines the preferred beam vector by exploiting the SCSI and only feeds back CQI at each timeslot. The performance of the MBS-SCF scheme is nearly the same as the conventional scheme. In order to further improve the sum rate, we propose multi-beam selection with dual CQIs feedback (MBS-DCF) scheme, which determines dual statistical eigen-directions and feeds back dual CQIs at each timeslot. It will increase the opportunity to exploit multiuser diversity and multiplexing gain. Simulation results demonstrate that the MBS-DCF scheme exhibits a higher sum rate than the conventional scheme does.