Fluid antenna (FA) is an emerging technology in recent years to switch physical locations of antennas in a predetermined small space. This paper proposes a reconfigurable intelligent surfaces (RIS)-cooperative framework with FA-assisted non-orthogonal multiple access (NOMA) system, namely FA-RIS-NOMA. Targeting multi-interference scenarios in urban environments, the considered system incorporates a base station (BS) equipped with a conventional antenna and users each equipped with an FA. Additionally, the RIS is utilized to forward signals between BS and users blocked by obstacles. The non-line-of-sight multipath fading characteristics of the RIS-assisted link are modeled as a Nakagami-mchannel. To overcome the multiuser interference and improve the system performance, we develop the NOMA technique for resource allocation. Meanwhile, to reduce the signal processing complexity at the receiver, a group optimization greedy detection method is proposed. To evaluate the system’s reliability, the outage probability for each user is analyzed by using the copula function. This method enables the derivation of the cumulative distribution and probability density functions for the equivalent user-side channel, from which closed-form outage probability expressions are obtained. Numerical results demonstrate that the proposed framework achieves signal-to-noise ratio gains of approximately 9 dB and 8 dB over fixed-antenna and relay-assisted systems, respectively. Furthermore, the proposed detection method reduces computational complexity by over 75% with less than 0.5 dB performance degradation.

Spectrum scarcity and low utilization constrain the development of wireless communication. Cognitive radio enables sensor nodes (SNs) to monitor primary user (PU) channel usage, thereby enabling access to idle channels and improving spectrum efficiency. To address sensing impairments in wireless environments, cooperative spectrum sensing (CSS) has been implemented in cognitive wireless sensor networks (CWSNs) to ensure reliable spectrum detection. However, malicious SNs (MSNs) launching Byzantine attack can cause severe security threats to CSS. To ensure the effective operation of CSS against MSNs, this paper proposes a support vector machine (SVM) based on improved sand cat swarm optimization (SCSO), denoted as SCSO-SVM, for accurate MSN identification. The algorithm leverages the small-sample superiority of SVM and employs an enhanced SCSO to adaptively optimize its kernel and penalty parameters. In contrast to existing isolation forest (IF)-based algorithms and SVM methods based on other meta-heuristic algorithms, the proposed algorithm significantly reduces time consumption. Furthermore, it achieves high classification accuracy and F1-score with limited training samples. At last, simulation results demonstrate that across various Byzantine attack scenarios, the proposed SCSO-SVM algorithm reduces the time consumption by an order of magnitude compared to IF and spectral clustering -based fusion algorithm (IFSC), improved IF algorithm, particle swarm optimization (PSO)-based SVM (PSO-SVM), and grey wolf optimizer (GWO)-based SVM (GWO-SVM), while maintaining high accuracy.

In the context of Internet of Things (IoT) networks, this work investigates an active reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system and establishes an integrated analytical frame-work that rigorously characterizes both the inherent double-fading effect of RIS-assisted propagation and the power-gain coupling introduced by the active RIS. A weighted sum-rate (WSR) maximization problem is then formulated under the constraints of access point (AP) power budgets, the RIS power consumption limitations, and the receiver energy harvesting requirements. To manage the resulting highly non-convex structure, the original problem is equivalently reformulated as a weighted minimum mean squared error (WMMSE) minimization problem, which enables the development of an efficient alternating optimization algorithm. At each iteration, the MMSE decoding vectors are firstly updated in closed form, while the energy signal covariance matrices, the AP information beamformers, and the active RIS reflection coefficients are subsequently optimized via semi-definite programming, successive convex approximation, and augmented Lagrangian method, respectively. Extensive simulation results demonstrate that the adoption of an active RIS effectively mitigates the double-fading effect and achieves performance comparable to that of passive RIS with significantly fewer reflecting elements, while the proposed joint optimization strategy yields substantial WSR gains and further enlarges the performance gap between the optimized active and passive RIS schemes.

Cooperative Spectrum Sensing (CSS) is vulnerable to hybrid Byzantine attack (HBA) from malicious users (MUs). Existing defense mechanisms, such as hard/soft fusion and reputation-based models, often struggle to adapt to dynamic attack patterns and fluctuating noise environments. For this aim, this paper proposes a self-parameterized and adaptive defense framework, termed dynamic Byzantine detection (DBD) to achieve robust Byzantine identification in an unsupervised manner. Following a state-change detection paradigm, we analyze the relationship between energy measurements from consecutive sensing periods and transform the problem into detecting distribution shifts to eliminate the reliance on prior “clean” data or ground-truth labels. Then, we further employ an improved hierarchical density-based clustering algorithm, with parameters self-determined via ak-distance analysis, to identify and effectively remove MUs across multiple levels. In a complex time-varying attack sequence involving independent and collusive MUs, DBD consistently achieves high detection accuracy and strong stability, while most benchmarks exhibit significant performance degradation. Under severe noise power fluctuation, DBD demonstrates superior robustness compared to an online support vector machine (SVM) that relies on ground-truth training data. Furthermore, a series of numerical simulation results demonstrate that DBD achieves notably superior performance over traditional methods while operating more efficiently than an online SVM, presenting a practical and effective solution for securing CSS.

Recent research on Unmanned Aerial Vehicle (UAV) localization has focused on enhancing accuracy by incorporating angle information. However, this requires array antennas on UAVs, which increases costs and reduces flight endurance. To address this, we propose a distributed, collaborative localization method for UAV swarms, integrating Reconfigurable Intelligent Surface(RIS) with Multidimensional Scaling (MDS). This method relies solely on inter-UAV ranging and a subset of UAVs equipped with Global Positioning System (GPS). The proposed algorithm improves localization by leveraging inter-UAV ranging, RIS, and GPS-equipped UAVs. After swarm patching, an improved node selection algorithm, based on the Nyström approximation, directs RIS-equipped UAVs to designated areas. The UAV distance matrix is refined using the 3σ Gaussian principle and Rice distribution criteria, while MDS computes relative coordinates within each patch. Simulations show that the algorithm reduces localization error, achieving over 10% improvement using multiple RIS-equipped UAVs. Furthermore, the proposed algorithm reduces GPS-equipped UAV requirements by deploying passive RIS-equipped UAVs, extending swarm operation duration and enhancing localization accuracy.

Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on learned conditional and co-occurrence probabilities. At the transmitter, the algorithm filters redundant relations and entity pairs, while at the receiver, semantic recovery leverages the same probability graphs to reconstruct omitted information. For further research, we also put forward a multi-round semantic compression algorithm with its theoretical performance analysis. Simulation results demonstrate that our semantic-aware scheme achieves superior transmission throughput and satiable semantic alignment, validating the efficacy of leveraging high-level semantics for image communication.

A selective soft-message-forward (SSMF) cooperation with new threshold decision (TD)-based detection is presented to improve the bit error ratio (BER) performance and power efficiency in two-way physical-layer network-coded multiple-input-multiple-output (MIMO) Internet of Things (IoT) systems. First, the SSMF-based physical-layer network coding (PNC) is proposed by selecting appropriate received signals for soft decoding at relay nodes for better BER performance and low complexity. The signals least affected by noises after zero forcing detection are mapped into log-likelihood ratio metrics for most reservation of the soft messages. Second, a new TD-based detection is obtained by classifying the amplified received signals into different processing regions to reduce false decoding for better performance. Finally, an adaptive power allocation (APA) for the SSMF-based PNC is given to further improve power efficiency. By adaptively allocating the transmitting power in source nodes, the amplitudes of two received signals are similar at the relay and thus efficiently eliminate the near-far effect. Simulation results indicate that the SSMF-based PNC surpasses the existing PNC with a binary phase-shift keying (BPSK) modulation. At BER of$10^{-3}$, the SSMF-based PNC outperforms the selective decode-and-forward (DF), SMF, and DF schemes by approximately 0.6, 1.0, and 1.7 dB, respectively. At BER of$10^{-3}$, the APA scheme outperforms the equal power allocation one by about 2 dB. Therefore, it can be efficiently used in two-way integrated satellite and ground wireless MIMO IoT systems.

In the realm of cognitive radio (CR), the soft and hard combinations are commonly applied for the fusion rule of cooperative spectrum sensing (CSS) to detect the primary user (PU) signal for available vacant spectrum resources and allow cooperative secondary users (SUs) to opportunistically access the channel without harmful interference to the PU’s normal communication. However, in the process of submitting the sensing information to the fusion center (FC), the soft combination requires great communication overhead from the SU to the FC, and the hard combination reports only one bit of the local decision resulting in unsatisfactory detection performance. In view of this, in this paper we make an in-depth investigation on the quantization of raw measurement data and propose a quantization-based multi-bit combination method that utilizes the information of each sampling point. On the basis of the proposed multi-bit combination approach, each SU quantifies the information obtained from the local sensing information and transmits the quantified sensing data in a few bits to the FC. Furthermore, we formulate an optimization problem for the error probability of the multi-bit combination to achieve the optimal CSS performance. Finally, numerical simulation results confirm the effectiveness and robustness of the proposed multi-bit combination method, establishing its superiority in various environments, in terms of the overall error probability.

With the development of communication technology, the Internet of Vehicles (IoV) is becoming increasingly important, enabling vehicle-to-everything communication for real-time information exchange and processing, thereby significantly enhancing traffic efficiency and safety. In this article, we consider a joint beamforming design problem in IoV, where the objective is to minimize transmission power, computation rate, and communication rate within the integrated sensing, communication, and computation (ISCC) framework. Moreover, reconfigurable intelligent surfaces (RISs) can provide additional spatial degrees of freedom to enhance the performance of ISCC systems in IoV within limited spectrum, energy resources, and complex interference management. To address the joint beamforming design problem, we present a cooperative beamforming algorithm called weight performance optimization (WPO), which explores three single-objective optimization problems in sensing, computation, and communication within the IoV context, using alternating optimization (AO) to simplify and solve these foundational elements of the WPO framework within limited resources and vehicle mobility, enhancing resource distribution while maintaining a balance between power efficiency and system performance. Numerical results demonstrate the efficiency and potential advantages of our proposed algorithms. Specifically, the results show that the sensing error of the WPO algorithm is reduced by up to 92.2% compared to existing popular algorithms, while the computation rate and communication rate are increased by more than 29.5% and 23.9%, respectively.

This letter investigates the design and optimization of secure beamforming in an intelligent reflecting surface (IRS)-assisted multiuser cognitive radio simultaneous wireless information and power transfer (CR-SWIPT) system. The proposed method leverages IRS to address cognitive energy harvesting nodes as potential eavesdroppers (Eve). The objective is to maximize the achievable secrecy rate while satisfying multiple constraints, such as transmit power control, energy harvesting, phase shifts, and maximum tolerable interference power. To solve this highly non-convex optimization problem, we propose a hybrid algorithm that combines successive convex approximation (SCA) and inexact block coordinate descent (IBCD). By decomposing the problem into three sub-problems, local optimal beamforming matrices and phase-shift matrices are obtained using the SCA method and complex circle manifold (CCM) method, respectively. Simulation results show that the secrecy rate at the SWIPT information decoding node in the IRS-assisted multiuser CR-SWIPT system improves significantly, with an approximate 40% enhancement compared to the random phase shift scheme with maximum transmit power. The effectiveness of the proposed algorithm is further validated through secrecy rate performance evaluations under different system configurations.

To solve low-resource utilization, complex storage, and busy traffic in space–air–ground integrated networks (SAGINs), a new multilayered decentralized coded caching (ML-DCC) scheme is proposed in hierarchical networks with nonuniform file popularity and multilevel cache capacity. First, a file popularity prediction is performed by a random forest model with grid search. Second, a multipopularity coded caching (MPCC) strategy by grouping files is executed in a two-hop network with fixed cache size. Finally, a new ML-DCC scheme is proposed to adopt nonuniform file popularity and multilevel cache capacity with both block-coded caching and zero-bit padding to obtain high-resource utilization and efficient file exchange in file transmissions. The innovations are random forest classifier with bagging integration and grid search to improve network traffic and link load, decentralized coded caching to reduce average transferred files, and coded caching and grouping files by popularity to improve network load. Simulation results show that the data payload of the proposed scheme is significantly improved by about 1.88, 1.18, 1.38, 3.53, and 4.39 times, when compared with those of the shared cache, multilevel popularity, hierarchical coded caching, MPCC, and uncoded caching schemes, respectively. Under the expected load of$R=211.57F$bits of the shared link, the cache size used by the proposed ML-DCC is only 1/42.85 and 1/24.39 of those of the highest popularity first (HPF) and nonuniform cache schemes, respectively.

To both eliminate interference from unrequested subfiles and improve sum degree-of-freedom (DoF) in multirelay Internet of vehicles (IoV) systems, a new decentralized multi-subsystem weighted interference cancellation (DMS-WIC) scheme with coded caching is proposed. It divides the transmission into two stages with optimized parameters to minimize link load. It also improves the sum DoF by jointly combining the weighted interference cancellation, orthogonal unicast, and decentralized coded multicast together. Given the number of interference-free subfiles larger than that of users, traditional interference cancellation wastes cache capacities of both users and relays by ignoring partial cache capacities and thus it fails to achieve a balance between the number of both users and interference cancellation subfiles. The DMS-WIC achieves additional DoF by adopting orthogonal unicast and subsequently dividing cache of both users and relays compared with those of traditional interference cancellation. In other cases, the weighted requested subfiles are transmitted by combining physical layer file division and network layer coding delivery together to achieve interference alignment and cancellation gains. The DMS-WIC adaptively adjusts files and subsystem partition parameters by perceiving systematical parameters. Besides, a multirelay cooperative coded caching is introduced to further reduce transmission and complexity. Given the number of relays larger than that of users, the relays deliver coded subfiles with different user sets. Otherwise, a relay skips the fix of the user number units, until all user sets are taken after the completion of a transmission. Simulation results show that the proposed DMS-WIC with coded caching achieves more than 10.6% sum DoF gain compared with that of traditional interference cancellation.

Information-centric networking (ICN) can efficiently utilize bandwidth resources and reduce network data transmission latency, but coded multicast gains are not considered. A novel multiple-subsystem nested coding caching (MSNCC) is then proposed in hierarchical multirelay wireless sensor space-air-ground integrated network (SAGIN) with distinct sensor cache capacities and coded caching for potential coded caching gains. The group-based and zero-bit padding coded caching schemes are tightly coupled with two divided subsystems: 1) network layer placement and 2) physical-layer delivery. Through decentralized caching status (DCSs) aware coded caching optimization, the first subsystem achieves caching gains between adjacent layers, and the second subsystem directly exploits coded multicast gains between a server and several sensors. In situation I of the number of sensors larger than that of files, the MSNCC adopts the group-based decentralized caching to transmit coded subfiles between relays and sensors. In situation II of the remaining scenarios, the zero-bit padding coded caching improves link rate and system complexity. The innovation is the MSNCC with distinct sensor cache capacities by combining group-based decentralized, zero-bit padding, and hierarchical caching together. It effectively improves the link load in the worst circumstance by exploiting potential coded multicast opportunities among relays for different transmissions. Simulation results indicate that, in situation I, the MSNCC obtains approximately 38.5% and 26.5% reduction in delivery rate compared with those of uncoded caching and zero-bit padding coded caching, respectively. It also achieves about 33.3% rate gains compared with that of the uncoded caching.

Optimized polar-coded cooperation with an approximate prior probability-based threshold decision is proposed to improve the reliability and complexity in selective decode-and-forward (SDF) cooperation of multiple satellite constellation systems. First, an SDF scheme with polar coding is adopted in cooperative communications for efficient code construction and relay forwarding. Second, in the logarithmic belief propagation (BP) polar decoding, the variable iterative threshold is derived with independent and variable signal-to-noise ratio (SNR)-based approximate prior probability. This threshold reduces complexity by eliminating active nodes with high reliability in each decoding iteration. Third, the information detection factor is introduced to evaluate the accuracy of decoded bits, thereby limiting the average decoding iterations to improve decoding efficiency. Simulation results show that the proposed polar-coded SDF cooperation obtains 1.5 dB performance gain at a bit error rate (BER) of 10−3compared with that of existing LDPC-related schemes. The complexity of the optimized logarithmic BP decoding is reduced by 25%–50% compared with that of the traditional BP decoding. Therefore, the proposed scheme has good BER performance and low complexity in cooperative communications of multisatellite and wireless integrated systems.

Cooperative spectrum sensing (CSS) has been considered as a powerful approach to improve the utilization of scarce radio spectrum resources. However, spectrum sensing data falsification (SSDF) can hugely degrade the achievable detection accuracy of the primary user (PU) channel status in cognitive radio (CR) networks and thus requires learning from past CR network environments. Using the supervised learning (SL) method, a large number of labels about the real state of the channel availability need to be acquired. In a practical setting, however, obtaining off-the-shelf label data is a challenging undertaking because it necessitates cooperation among PUs and secondary users (SUs) and increases communication overhead in CR networks. Motivated by this, we solve the issue of label data scarcity in CR networks in this paper, and propose a semi-supervised learning-enabled two-stage framework for CSS against SSDF attack, in which combines the superior performance of semi-supervised support vector machine (S3VM) and the fast convergence of K-means. At last, simulation results demonstrate that S3VM is equipped with a greater detection performance than support vector data description (SVDD), and the same detection performance as support vector machine (SVM), especially it also the robustness and excellent performance using a small amount of labeled data.

An incremental selection hybrid decode-amplify forward (ISHDAF) scheme for the two-hop single relay systems and a relay selection strategy based on the hybrid decode-amplify-and-forward (HDAF) scheme for the multirelay systems are proposed along with an optimized power allocation for the Internet of Thing (IoT). Given total power as the constraint and outage probability as an objective function, the proposed scheme possesses good power efficiency better than the equal power allocation. By the ISHDAF scheme and HDAF relay selection strategy, an optimized power allocation for both the source and relay nodes is obtained, as well as an effective reduction of outage probability. In addition, the optimal relay location for maximizing the gain of the proposed algorithm is also investigated and designed. Simulation results show that, in both single relay and multirelay selection systems, some outage probability gains by the proposed scheme can be obtained. In the comparison of the optimized power allocation scheme with the equal power allocation one, nearly 0.1695 gains are obtained in the ISHDAF single relay network at a total power of 2 dB, and about 0.083 gains are obtained in the HDAF relay selection system with 2 relays at a total power of 2 dB.

By introducing amplify‐and‐forward relaying into a cognitive radio system, typical cognitive relay networks are studied for the optimisation problems of the spectrum and power allocation. By applying the mixed spectrum access of overlay and underlay approaches, an interference evaluation strategy is proposed to use different spectrum and power allocation methods while the secondary users (SUs) are located in different service regions of the primary users (PUs). In the interference evaluation strategy, the service area of the PUs is divided according to the possible interference strength of the PUs from the SUs compared with the location‐aware strategy in which the service area is divided only by the location information. An optimal power allocation algorithm is developed to maximise the throughput of the SUs under the condition of anti‐interference performance of the PUs and the total power constraints of the SUs. A type of computing algorithm that joins the subgradient and the Newton's method is used in order to resolve the complex optimisation problem. Numerical results show that the performance using the interference evaluation strategy, such as the throughput, the power consumption, and the energy efficiency, outperforms that using the location‐aware strategy.

An improved construction scheme of efficient extended irregular repeat-accumulate (eIRA) codes with easy check matrix encoding is proposed in this paper. At the dual diagonal parts of the right check matrices of the codes, the distances of the nodes are enlarged in their corresponding Tanner graphs by increasing the distances between the adjacent “1” of the matrices. Thus, the probabilities of small loops in the Tanner graphs of the codes are reduced. Therefore, it decreases the error self-feedback and obtains good decoding performance. Moreover, with an extrinsic information transfer (EXIT) chart method, the degree profile of the code can be optimally designed to meet the requirement of optimal decoding performance. Simulation results show that our proposed coding scheme slightly outperforms the contrast original eIRA code about 0.05–0.1 dB at a bit-error-ratio (BER) of 10−5with the same binary phase shift keying (BPSK) system in an additive white Gaussian noise (AWGN) channel. In addition, it obtains lower encoding and decoding complexity since it employs the sparse check matrices for encoding and applies the easy addressing operations both for encoding and decoding. Therefore, the proposed scheme of constructing eIRA codes can be efficiently applied in wireless digital communications.

Turbo codes are near Shannon limit channel codes widely used in space communication systems and so on at low signal-to-noise rate (SNR). Timing recovery is one of the key technologies for these systems to work effectively. In this paper, an efficiently iterative timing recovery with Turbo decoding is presented. By maximizing the sum of the square of soft decision metrics from Turbo decoding, it can obtain accurate timing acquisition. And a computation-efficient approximate gradient descent method is adopted to obtain rough estimate of timing offset. Another merit of it is that, by the proposed method, a rate-1/6 Turbo coded binary phase shift keying (BPSK) system can even work at very low SNR (Es/N0) about -7.44 dB without any pilot symbol. Finally, the whole timing recovery scheme is accomplished where the proposed method is combined with Mueller-Muller (M&M) timing recovery which performs the timing track. Simulation results indicate that the Turbo coded BPSK system with rather large timing errors by the proposed scheme can achieve performance within 0.1 dB of the ideal code with reasonable computations and storages.

A new scheme of the extended irregular repeat- accumulate (eIRA) coded tamed frequency modulation (TFM) for deep space communications is introduced in this paper. It is mainly based on the optimal concatenation of the eIRA code and the continuous phase encoder (CPE) decomposed from TFM. In order to obtain good performance of the eIRA coded TFM, the main principle of our scheme is to jointly optimize the concatenation by eliminating the short loops in the Tanner graph of the eIRA code and the coding part of TFM and to jointly decode between them. Then our scheme without interleaving and the contrast scheme with interleaving are simulated in an additive white Gaussian noise (AWGN) channel which is typical in deep space communications. Simulation results have shown that our scheme can obtain lower complexity and less decoding delay (due to the reduction of interleaving) at the cost of just 0.1-0.15 dB performance loss when compared to the scheme with interleaving given bit-error-ratio (BER) of 10 5. Therefore, our scheme can be used to implement the coded TFM system for deep space communications with good performance and low complexity.