This paper investigates the uplink spectral efficiency of distributed cell-free (CF) massive multiple-input multiple-output (mMIMO) networks with correlated Rayleigh fading channels based on three different channel estimation schemes. Specifically, each access point (AP) first uses embedded pilots to estimate the channels of all users based on minimum mean-squared error (MMSE) estimation. Given the high computational cost of MMSE estimation, the low-complexity element-wise MMSE (EW-MMSE) channel estimator and the least-squares (LS) channel estimator without prior statistical information are also analyzed. To reduce non-coherent and coherent interference during uplink payload data transmission, simple centralized decoding (SCD) and large-scale fading decoding (LSFD) are examined. Then, the closed-form expressions for uplink spectral efficiency (SE) using MMSE, EW-MMSE, and LS estimators are developed for maximum ratio (MR) combining under LSFD, where each AP may have any number of antennas. The sum SE maximization problem with uplink power control is formulated. Since the maximization problem is non-convex and challenging, a block coordinate descent approach based on the weighted MMSE method is used to get the optimal local solution. Numerical studies demonstrate that LSFD and efficient uplink power control can considerably increase SE in distributed CF mMIMO networks.

In this paper, an index modulation (IM) aided uplink orthogonal time frequency space modulation (OTFS) structure for sparse code multiple access (SCMA) is proposed. To be more specific, the information bits are firstly partitioned for transmit antenna (TA) selection and sparse codeword mapping, respectively. Subsequently, the codewords deployed on the 2-dimensional (2D) delay-Doppler (DD) plane are transmitted by the selected TA, and the superimposed signals are jointly detected at the receiver. Furthermore, a low-complexity zero-embedded expectation propagation (ZE-EP) detector is conceived, where the codebooks are extended with zero vectors to reflect the silent indices. The simulation results demonstrate that the proposed IM-OTFS-SCMA system is capable of providing significant performance gain over the OTFS-SCMA counterpart.

New communication systems require high spectral and energy efficiencies to meet the growing demand for services in future networks. In this paper, an efficient multiple parallel reconfigurable intelligent surfaces (RIS)-assisted multiuser (MU) multiple input-multiple output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system is presented. In the transmitter, the proposed N-RIS-MU-MIMO-DQSM system uses a modified block diagonalization technique and a genetic algorithm (GA) to jointly design the precoding signals required at the base station (BS) and the optimal phase changes required at multiple RISs. A reduced detection complexity and improved bit error rate (BER) performance are achieved by incorporating spatial modulation. The proposed system is compared under the same conditions and parameters with two reference systems, considering blind and optimized RISs approaches over correlated Rayleigh fading channels. Results show that compared with a similar system that does not use RISs, the proposed system has up to 30 dB gain in BER performance. Compared with a similar system based on conventional quadrature amplitude modulation (QAM), the proposed system has gains of up to 2-3 dB in BER performance and up to 55.8% lower detection complexity for the analyzed cases.

As a novel signaling technology, the power splitting receiver (PSR) simultaneously employs both the coherent and non-coherent signal processing. In order to improve its communication performance, an intelligent reflecting surface (IRS) is introduced into its signal propagation path. Consequently, an IRS-aided PSR is concerned for a point-to-point (P2P) data link, where both the single-antenna and multi-antenna deployments on the receiver are discussed. We aim at maximizing the capacity of the concerned P2P data-link by jointly optimizing the passive beamforming of IRS and the splitting ratio of PSR, either in single-antenna or multi-antenna case. However, owing to the coupling of multiple variables, the optimization problems are non-convex and challenging, especially in the later multi-antenna case. The proposed alternating-approximating algorithm (A-A), aided by semi-definite relaxation (SDR) and successive convex approximation (SCA) methods, etc., successfully overcomes these challenges. We compare the IRS-aided PSR system that optimized by our proposed algorithm to the systems without IRS or PSR, and the systems without joint optimization. The simulation results show that our proposal has a better performance.

As emerging services continue to be explored, indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues, leading to difficulties in achieving flexible coverage. In this paper, we introduce transmissive reconfigurable intelligent surfaces (RISs) as intelligent passive auxiliary devices into indoor scenes, replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs. Specifically, we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multi-user downlink communication systems. This involves considering more realistic indoor congestion modeling and near-field propagation characteristics. The goal of our optimization is to minimize the total transmit power at the access point (AP) for different user service requirements, including quality-of-service (QoS) and wireless power transfer (WPT). Due to the non-convex nature of the optimization problem, adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming. Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios. Furthermore, the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.

This paper proposes a genetic optimization method for the construction of non-binary quasi-cyclic low-density parity-check (NB-QC-LDPC) codes with short block lengths. In our scheme, the initial template base matrices and the corresponding non-binary replacement matrices are constructed by the progressive edge growth algorithm and randomly generated, respectively. The genetic algorithm is then utilized to optimize the base matrices and the replacement ones. The simulation results show that the NB-QC-LDPC codes constructed by the proposed method achieve better decoding performance and lower implementation complexity compared to the existing NB-LDPC codes such as consultative committee for space data system and BeiDou satellite navigation system.

High-selectivity common-mode (CM) and differential-mode (DM) reflectionless balanced bandpass filters (BBPFs) are proposed in this article. By loading absorption networks at single/both ends of the basic ring resonator, input-/two-port wideband CM and DM reflectionless performance, wideband filtering performance and all-stop CM suppression are obtained. The absorption network composed of K-sections of coupled-lines (CLs) terminated with grounded resistors can not only extend the filtering performance to high order, but also realize wideband absorption of CM noise and out-of-band DM signals. Absorptive stubs are loaded at ports to increase the design flexibility and enhance the absorption. As for the input-reflectionless type, multiple independently controlled transmission zeros (TZs) are obtained by the TZ control network to improves the selectivity and out-of-band rejection. A set of 2 GHz micro-strip BBPFs are designed and measured, which shows simultaneous CM and DM absorption performance.

This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the presence of multi-cell interference. Specifically, this paper focuses on both channel modelling and receiver design for interference estimation and mitigation. We propose a delay-calibrated block-wise linear model, which extracts the delay of the dominant tap of each interference as a key parameter and approximates the residual channel coefficients by the recently developed block-wise linear model. Based on the delay-calibrated block-wise linear model and the angle-domain channel sparsity, we further conceive a message passing algorithm to solve the channel estimation problem. Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.

In this paper, we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing (MIMO-OFDM) dual-functional radar-communication (DFRC) system, which enables simultaneous communication and sensing in different subcarrier sets. To obtain the best tradeoff between communication and sensing performance, we first derive Cramer-Rao Bound (CRB) of targets in detection area, and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power. To tackle the non-convex mixed integer programming problem, we decompose the original problem into a semidefinite programming (SDP) problem and a convex quadratic integer problem and solve them iteratively. The numerical results demonstrate the effectiveness of our proposed algorithm, as well as the performance improvement brought by optimizing radar receivers selection.

The complexity of underwater environments renders underwater acoustic signals vulnerable to various forms of noise during transmission, creating significant challenges for signal demodulation tasks. This paper presents a novel demodulation method for multi-class single-carrier underwater acoustic signals. Our approach employs two innovative structures for modeling in the time and frequency domains, integrating these features for comprehensive discrimination. Specifically, we introduce a High-Efficiency Convolution (HEC) Block to extract time-domain waveform features and a Local-Global Attention (LGA) structure for time-frequency features, utilizing cross-attention to fuse these features. This method enables the network to learn hidden frequency, phase, and amplitude characteristics within high-dimensional features, effectively capturing both fine-grained local and long-distance global features. A classifier is then constructed to categorize multi-class modulation signals, completing the demodulation process. Simulation results highlight the method's exceptional performance: in a Gaussian channel with a signal-to-noise ratio (SNR) of 0 dB, the demodulation error rates for 2PSK, 2FSK, 2ASK, 4PSK, 4FSK, and 8PSK signals are all below 0.01, while the error rate for 16QAM modulated signals is less than 0.1. Additionally, validation using BELLHOP simulation data and real-world data collected from the Yellow Sea further demonstrates the proposed method's remarkable noise resistance and demodulation capabilities.

6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks. As a primary component of self-healing networks, fault detection is investigated in this paper. Considering the fast response and low time-and-computational consumption, it is the first time that the Online Broad Learning System (OBLS) is applied to identify outages in cellular networks. In addition, the Automatic-constructed Online Broad Learning System (AOBLS) is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting. Furthermore, a multi-layer classification structure is proposed to further improve the classification performance. To face the challenges caused by imbalanced data in fault detection problems, a novel weighting strategy is derived to achieve the Multi-layer Automatic-constructed Weighted Online Broad Learning System (MAWOBLS) and ensemble learning with retrained Support Vector Machine (SVM), denoted as EMAWOBLS, for superior treatment with this imbalance issue. Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.

Cloud computing (CC) provides infrastructure, storage services, and applications to the users that should be secured by some procedures or policies. Security in the cloud environment becomes essential to safeguard infrastructure and user information from unauthorized access by implementing timely intrusion detection systems (IDS). Ensemble learning harnesses the collective power of multiple machine learning (ML) methods with feature selection (FS) process aids to progress the sturdiness and overall precision of intrusion detection. Therefore, this article presents a meta-heuristic feature selection by ensemble learning-based anomaly detection (MFS-ELAD) algorithm for the CC platforms. To realize this objective, the proposed approach utilizes a min-max standardization technique. Then, higher dimensionality features are decreased by Prairie Dogs Optimizer (PDO) algorithm. For the recognition procedure, the MFS-ELAD method emulates a group of 3 DL techniques such as sparse auto-encoder (SAE), stacked long short-term memory (SLSTM), and Elman neural network (ENN) algorithms. Eventually, the parameter fine-tuning of the DL algorithms occurs utilizing the sand cat swarm optimizer (SCSO) approach that helps in improving the recognition outcomes. The simulation examination of MFS-ELAD system on the CSE-CIC-IDS2018 dataset exhibits its promising performance across another method using a maximal precision of 99.71%.

Software-Defined Perimeter (SDP) provides a logical perimeter to restrict access to services. However, due to the security vulnerability of a single controller and the programmability lack of a gateway, existing SDP is facing challenges. To solve the above problems, we propose a flexible and secure SDP mechanism named Mimic SDP (MSDP). MSDP consists of endogenous secure controllers and a dynamic gateway. The controllers avoid single point failure by heterogeneity and redundancy. And the dynamic gateway realizes flexible forwarding in programmable data plane by changing the processing of packet construction and deconstruction, thereby confusing the potential adversary. Besides, we propose a Markov model to evaluate the security of our SDP framework. We implement a prototype of MSDP and evaluate it in terms of functionality, performance, and scalability in different groups of systems and languages. Evaluation results demonstrate that MSDP can provide a secure connection of 93.38% with a cost of 6.34% under reasonable configuration.

The uncertain nature of mapping user tasks to Virtual Machines (VMs) causes system failure or execution delay in Cloud Computing. To maximize cloud resource throughput and decrease user response time, load balancing is needed. Possible load balancing is needed to overcome user task execution delay and system failure. Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration. Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs. Thus, the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism (HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly. This load balancing approach improved performance by considering average network latency, dependability, and throughput. This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation, assign jobs to VMs with more solution diversity, and prevent the solution from reaching a local optimality. Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan, 16.21% increase in mean throughput, and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA, HDWOA, and Binary Bird Swap.

In the sixth generation mobile communication (6G) system, Non-Terrestrial Networks (NTN), as a supplement to terrestrial network, can meet the requirements of wide area intelligent connection and global ubiquitous seamless access, establish intelligent connection for wide area objects, and provide intelligent services. Due to issues such as massive access, doppler shift, and limited spectrum resources in NTN, research on resource management is crucial for optimizing NTN performance. In this paper, a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided. Firstly, the key technologies involved in NTN resource management is summarized. Secondly, NTN resource management is discussed from network pattern and resource pattern. The network pattern focuses on the application of different optimization methods to different network dimension communication resource management, and the resource type pattern focuses on the research and application of multi-domain resource management such as computation, cache, communication and sensing. Finally, future research directions and challenges of 6G NTN resource management are discussed.

The utilization of unmanned aerial vehicle (UAV) relays in cooperative communication has gained considerable attention in recent years. However, the current research is mostly based on fixed base stations and users, lacking sufficient exploration of scenarios where communication nodes are in motion. This paper presents a multi-destination vehicle communication system based on decode-and-forward (DF) UAV relays, where source and destination vehicles are moving and an internal eavesdropper intercepts messages from UAV. The closed-form expressions for system outage probability and secrecy outage probability are derived to analyze the reliability and security of the system. Furthermore, the impact of the UAV’s position, signal transmission power, and system time allocation ratio on the system's performance are also analyzed. The numerical simulation results validate the accuracy of the derived formulas and confirm the correctness of the analysis. The appropriate time allocation ratio significantly enhances the security performance of system under various environmental conditions.

Multiple UAVs cooperative target search has been widely used in various environments, such as emergency rescue and traffic monitoring. However, uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states. This limitation hinders timely information sharing and insightful path decisions for UAVs, resulting in inefficient or even failed collaborative search. Aiming at this issue, this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments. Specifically, an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment. Based on the fused information, we formalize the trajectory planning as a multi-objective optimization problem by jointly considering search performance and UAV energy harnessing. A multi-agent deep reinforcement learning based algorithm is proposed to solve it, where the reward-guided real-time path is determined to achieve an energy-efficient search. Finally, extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.

The rapid development of the Internet of Vehicles (IoVs) underscores the importance of Vehicle-to-Everything (V2X) communication for ensuring driving safety. V2X supports control systems by providing reliable and real-time information, while the control system's decisions, in turn, affect the communication topology and channel state. Depending on the coupling between communication and control, radio resource allocation (RRA) should be control-aware. However, current RRA methods often focus on optimizing communication metrics, neglecting the needs of the control system. To promote the co-design of communication and control, this paper proposes a novel RRA method that integrates both communication and control considerations. From the communication perspective, the Age of Information (AoI) is introduced to measure the freshness of packets. From the control perspective, a weighted utility function based on Time-to-Collision (TTC) and driving distance is designed, emphasizing the neighboring importance and potentially dangerous vehicles. By synthesizing these two metrics, an optimization objective minimizing weighted AoI based on TTC and driving distance is formulated. The RRA process is modeled as a partially observable Markov decision process, and a multi-agent reinforcement learning algorithm incorporating positional encoding and attention mechanisms (PAMARL) is proposed. Simulation results show that PAMARL can reduce Collision Risk (CR) with better Packet Delivery Ratio (PDR) than others.

Distributed computing is an important topic in the field of wireless communications and networking, and its high efficiency in handling large amounts of data is particularly noteworthy. Although distributed computing benefits from its ability of processing data in parallel, the communication burden between different servers is incurred, thereby the computation process is detained. Recent researches have applied coding in distributed computing to reduce the communication burden, where repetitive computation is utilized to enable multicast opportunities so that the same coded information can be reused across different servers. To handle the computation tasks in practical heterogeneous systems, we propose a novel coding scheme to effectively mitigate the ``straggling effect'' in distributed computing. We assume that there are two types of servers in the system and the only difference between them is their computational capabilities, the servers with lower computational capabilities are called stragglers. Given any ratio of fast servers to slow servers and any gap of computational capabilities between them, we achieve approximately the same computation time for both fast and slow servers by assigning different amounts of computation tasks to them, thus reducing the overall computation time. Furthermore, we investigate the information-theoretic lower bound of the inter-communication load and show that the lower bound is within a constant multiplicative gap to the upper bound achieved by our scheme. Various simulations also validate the effectiveness of the proposed scheme.

In Internet of Vehicles, Vehicle-Infrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications. Federated Learning (FL) is the emerging computation paradigm to provide efficient and privacy-preserving collaborative learning. However, in IoV environment, federated learning faces the challenges introduced by high mobility of vehicles and non-Independently Identically Distribution (non-IID) of data. High mobility causes FL clients quit and the communication offline. The non-IID data leads to slow and unstable convergence of global model and single global model’s weak adaptability to clients with different localization characteristics. Accordingly, this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in IoV environment, including FedSA (Special Asynchronous Federated Learning with Self-adaptive Aggregation) for low-level FL between a Road Side Unit (RSU) and the vehicles within its coverage, and FedAtt (Federated Learning with Attention Mechanism) for high-level FL between a cloud server and multiple RSUs. Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic (A2C) algorithm. Experiments show the proposed strategy encourages vehicles to participate in global aggregation, and outperforms existing methods in training performance.