With the emergence of information overload in the Social Internet of Things (SIoT), personalized recommender systems have become essential for helping users locate the items they need. Effectively modeling the heterogeneous relationships across multiple information sources and weighting them according to their varying importance, especially under sparse social interactions in the SIoT, remains a key challenge. To address this issue, this paper proposes a Multi-source Adaptive Relational Graph Convolutional Network (MARGCN) framework for recommender systems. Useruser and useritem interactions are modeled as two relational graphs, and their embedded features are aggregated using graph convolutional networks (GCNs). A multi-source relationship perception model is designed to dynamically perceive and measure the importance of multiple relationships within the graph structure, thereby enhancing the ability to recognize heterogeneous information. An adaptive information fusion model is then constructed to dynamically integrate representations from different sources through a learnable fusion strategy, avoiding information loss or redundancy caused by simple weighting. Users and items are ultimately represented by aggregating and updating their embeddings via GCNs. Experiments show that compared with the most advanced methods, MARGCN improves the hit rate (HR) and the normalized discounted cumulative gain (NDCG) by 2.66%, 0.52%, and 3.93% in HR@5, HR@10, and HR@15 and by 1.58%, 1.32%, and 3.14% in NDCG@5, NDCG@10, and NDCG@15, respectively.

This paper proposes a promising framework of multi-functional service incorporating sensing targets (STs), information receivers (IRs), and energy receivers (ERs) in an active reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transfer (ISCPT) system. In the proposed system, we aim to maximize the weighted sum of the received radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the transmit beamforming at the multi-functional base station (MFBS), the coefficients of active RIS, and the radar receive filter coefficients. Meanwhile, the constraints of the SINR of IRs, energy harvesting (EH) requirements of ERs, the power budget for the MFBS and active RIS, and the amplification gain should be satisfied. To guarantee the generality of formulated problems, we further incorporate the self-interference effects of echo signals, multi-target echo interference, simultaneous detection of multiple STs, and a nonlinear EH model into the generalized system model. Due to the presence of echo interference and multi-target echo interference, the MFBS transmits the dedicated sensing signal with the communication to enhance the sensing performance. The formulated problem is tackled by developing an efficient alternating optimization (AO) algorithm combined with fractional programming (FP) and majorization-minimization (MM) techniques. Finally, the numerical results reveal the impact of system parameters on the sensing performance, the trade-off relationship between multiple functionalities, and the deployment strategy of RIS. The main findings are as follows: 1) Active RIS is remarkably superior to passive RIS for ISCPT systems, especially for closer to the receivers with a 40 dB performance gain. 2) Comparatively, the radar sensing SINR is more sensitive to the number of active RIS units, while the SINR of IRs is more sensitive to the number of antennas at the base station. These results demonstrate that the proposed system holds the potential for practical deployment.

With the rapid advancement of unmanned aerial vehicle (UAV) technology, UAV cell free massive multiple-input multiple-output (CF-mMIMO) systems demonstrate significant potential for enhancing wireless communication performance. However, optimizing energy efficiency (EE) to meet the growing demands of communication has become a critical research challenge. This paper explores resource allocation in the UAV CF-mMIMO uplink under ultra reliable and low latency communication (URLLC) constraints to improve EE. We first conduct a systematic analysis in the system model to identify key factors impacting EE. Accounting for these factors, an optimization problem is then formulated. To solve this problem, we employ a self-supervised deep neural network (SSDNN) composed of two submodules: One is to extract channel characteristics of all users equipment (UEs), and the other is to optimally allocate power for both UAVs and ground user equipments (GUEs) via a resource allocation layer. Numerical results show that our proposed method achieves higher EE compared to approaches that do not consider GUE interference.

To achieve ultrareliable and low-latency communication (URLLC) and support high density of wireless connections simultaneously, the sixth-generation Industrial Internet of Things (6G-IIoT) necessitates an expansion of antenna arrays and broader bandwidths, which suffers from high energy consumption. To address this issue, this article investigates a cell-free massive multiple-input-multiple-output (CF-mMIMO) system and designs an iterative search-based two-stage energy efficiency (EE) optimization algorithm for the uplink communication of the system. The first stage prioritizes reliability to ensure that all users meet the URLLC requirements regarding latency and reliability. The second stage maximizes the EE under URLLC-satisfied conditions. Considering that iterative search algorithms incur a high computational overhead and variable number of iterations, we further propose a convolutional neural network architecture (RACNN) to approximate an optimal resource allocation strategy and to realize the real-time and stable output. This structure extracts deep correlations among users from the global channel features. It takes strategies of multitask learning and weight loss adaptation to improve the model’s convergence speed. Furthermore, we employ deep transfer learning to adjust RACNN parameters to accommodate the potential of dynamic communication scenarios, thereby reducing the demand for training samples and training time overhead. Finally, the efficacy of the proposed algorithm, RACNN, and deep transfer learning is validated through experimental simulations.

In an ultra-dense Industrial Internet of Things (UDI-IoT) network with device-to-device (D2D) communication technology applied, interaction among a large number of industrial Internet of Things devices (IIoTDs) leads to heavily overlapping interference, and Age-of-information (AoI) sensitive service in the network is difficult to guarantee. In this paper, a learning-based robust resource allocation considering overlapping interference and AoI-sensitive services requirements (LRRA-OIAoISR) is proposed to solve the problem of AoI resource management for the UDI-IoT networks with overlapping interference. We first construct the interference hypergraph model and analyze the interference relationship between D2D devices, which can effectively improve the utilization of spectrum resources. Then, an AoI model was constructed to address the resource management issues of AoI-sensitive services, and a robust optimization model was established under imperfect CSI. This model considers power control and resource conflict constraints to achieve maximum network throughput. Finally, to solve this robust optimization problem, we propose an LRRA-OIAoISR algorithm based on learning theory. The algorithm obtains an optimal robust optimization solution by reducing the impact of imperfect CSI. The performance of the proposed resource allocation method was verified through simulation. Compared with other benchmark algorithms, the proposed algorithm improves energy efficiency by an average of 52.5%, interference efficiency by an average of 88.5%, and throughput by an average of 125%.

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN, where different types of channel state information (CSI) including no CSI, instantaneous CSI, and delayed CSI, are considered. According to the types of CSI, interference alignment, beamforming, and space-time precoding are designed at the satellite and terrestrial transmitter side, and meanwhile, the UAV-RIS is introduced for the cooperating interference elimination process. Additionally, the degrees of freedom (DoF) obtained by the proposed IM scheme are discussed in depth when the number of antennas on the satellite side is insufficient. Simulation results show that the proposed IM scheme improves the system capacity in different CSI scenarios, and the performance is better than the existing IM benchmarks without UAV-RIS, but the performance improvement is at the cost of the requirement on the elements of UAV-RIS.

In this paper, we investigate a cognitive multicast communication system aided by active reconfigurable intelligent surface (active RIS). Specifically, for an underlay spectrum sharing cognitive multicast network, a cognitive radio base station (CRBS) communicates with secondary users (SUs) assisted by an active RIS. Meanwhile, the interference to primary users (PUs) is suppressed within the constraints of the transmit power of both the CRBS and active RIS, together with the restriction of the active RIS amplitude gain. We aim at the fairness problem for maximizing the minimum signal-to-interference-plus-noise-ratio (SINR) via joint beamforming design at the CRBS and the active RIS. To cope with this problem, the optimization and deep reinforcement learning (DRL) based algorithms are proposed. Specifically, the decision variables are decoupled by the alternating optimization (AO) method and then, the non-convex problem is transformed into a solvable convex form by using the successive convex approximation (SCA), Schur complement, and penalty convex-concave procedure (PCCP) methods. Furthermore, we design an AO-based algorithm for the formulated problem. Due to the characteristics of both exploration and exploitation, the DRL-based algorithms outperform the AO-based algorithm with proper parameter settings. Meanwhile, the DRL algorithm inherits the advantages of low execution complexity. The original optimization problem is first converted into a Markov decision process (MDP) form in DRL. Due to the complex objective function and various restrictions of power/amplification gain budget and quality of service (QoS), the constraints are categorized as the switching constraints for action adjustment and performance constraints for reward function setting, respectively. Subsequently, a segmented incentive-based reward function is developed to attain higher performance on SINR. We also propose two effective deep deterministic policy gradient (DDPG)-based and twin delayed deep deterministic policy gradient (TD3)-based algorithms. Finally, the simulation results demonstrate a notable enhancement in system performance upon the introduction of active RIS compared to the case with a passive RIS and the case without using an RIS. Moreover, with appropriately configured parameters, DRL algorithms outperform the AO-based algorithm, and notably, the TD3 algorithm is superior to the DDPG algorithm in optimization effectiveness.

In MIMO multi-user networks, inter-user interference (IUI) significantly affects the system performance. To handle this problem, this paper proposes the reconfigurable intelligent surface assisted cooperative interference alignment scheme (RIS-CIA). The core idea of this work is that the base station and full-duplex users jointly design space-time precoding matrices, which can reduce the dimension of the interference space on the user side. Besides, the additional interference caused by the information exchange process is split into sub-blocks by space-time precoding, then eliminated by interference nulling assisting by the passive RIS. The simulation results show that the RIS-CIA scheme with few numbers of elements obtains higher DoF than that of benchmark schemes with a huge number of elements.

In heterogeneous networks (HetNets), although deploying numerous small base stations (SBSs) can effectively enhance spectral efficiency (SE), it is difficult to achieve seamless coverage due to their fixed locations. To handle this issue, we propose a HetNet structure assisted by unmanned aerial vehicles (UAVs), where the high mobility and flexible deployment of UAVs are leveraged. However, interference is inevitable in the proposed UAV-assisted HetNet, thus we design a comprehensive interference management (IM) scheme, selectively adopting successive interference cancellation (SIC) algorithm, space-time interference alignment (STIA) and interference steering (IS) according to the location of users and interference types. The numerical results verify that with SIC-STIA-IS scheme, the proposed UAV-assisted HetNet is advantageous in degrees of freedom (DoF) and sum rate.

In this paper, the performance of a multiple-in-single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted by simultaneous trans-mitting and reflecting reconfigurable intelligent surface (STAR-RIS) under fading channel is studied. Firstly, the joint BS active beamforming and STAR-RIS passive beamforming are discussed. Based on that and using the Gamma approximation method, the statistical characteristics of the equivalent cascaded channels for BS-IR and BS-ER assisted by STAR-RIS are analyzed and derived, including the first-order and second-order moments, as well as the distribution function (CDF) and probability density function (PDF). Furthermore, we define and derive the rate outage probability of IR and power outage probability of ER and their approximate expressions at high SNR. Finally, the theoretical analysis results are verified by numerical simulations, and it is confirmed that the number of STAR-RIS units has positive effects on improving the system outage performance.

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

To accommodate the explosive growth of the Internet of Things (IoT), incorporating interference alignment (IA) into existing multiple access (MA) schemes is under investigation. However, when it is applied in MIMO networks to improve the system capacity, the new problem regarding information delay arises which does not meet the requirement of low-latency. Therefore, in this paper, we first propose a new metric named degree of delay (DoD) to quantify the issue of information delay. By analyzing DoD with classical transmission schemes, it can be seen that the information latency does affect the performance of the system. To cope with this issue, hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR) is first proposed. It achieves optimal performance in 2-user MIMO scenarios, but suffers a performance loss in$K$-user MIMO scenarios. Then, the improved HAA-PIE-RIR scheme (HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR) are proposed. The former achieves optimal performance in$K$-user MIMO scenarios, but requires heavy computational cost. The latter is a trade-off scheme considering performance and computational cost comprehensively. Overall, our proposed schemes can obtain lower DoD and higher DoF than that of traditional IA schemes.

Intelligent reflecting surface (IRS) is considered as an effective technology to enhance the performance of wireless communication systems. In this paper, the robust optimization design of the IRS-assisted wireless multi-group multicast MISO system with statistical CSI errors is investigated. Two optimization problems, namely max-min fairness problem and QoS problem, are discussed separately. In order to deal with the non-convex imperfect CSI constraint, the Bernstein-type inequality is utilized to transform the outage probability constraint into a second-order cone (SOC) constraint and linear inequalities. Furthermore, two efficient algorithms based on alternating optimization (AD) are proposed to solve the reformulated problems, respectively. In particular, the semi-definite relaxation (SDR) technique is applied to optimize the transmit beamforming and IRS reflection coefficients. The numerical simulation results indicate that by deploying IRS and utilizing the proposed algorithms, the system performance can be improved significantly. However, the gain of introducing IRS in the system heavily depends on the bound of the CSI error.

As a novel method to increase channel capacity of networks, interference alignment (IA) technique has attracted wide attention in fifth-generation wireless networks (5G). However, when it is applied in interference networks, the problem regarding information delay arises which has not been well addressed yet. In this paper, we formally propose the novel concepts of degree of delay (DoD) to quantify the issue of information delay and analyze its determining factors, i.e., delay sensitive factor, queueing delay slot and size of dataset. To reduce DoD, three novel joint IA schemes are proposed for broadcast channel (BC) networks with different amounts of users, i.e., hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme (HAA-IPIE-RIR) and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR). Among the three, the second scheme extends the application scenarios of the first scheme from 2-user to K-user while bringing huge computational complexity burden. The third scheme relieves the burden but it leads to slight degree of freedom (DoF) loss. Overall, the proposed schemes achieve higher DoF and lower DoD than the existing IA schemes.

Abstract This paper discusses an artificial noise‐aided intelligent reflecting surface‐MIMO–OFDM system physical layer secure communication, in which two cases for the intelligent reflecting surface reflection coefficient models are considered separately, that is unit modulus constraint for the reflection coefficients and the more practical situation of amplitude phase‐shift dependence. Then the problem of joint optimisation for the precoding matrix, artificial noise covariance matrix and intelligent reflecting surface reflection coefficient matrix to maximise the sum secrecy rate under the power constraint at the transmitter is formulated, and then an alternate optimisation‐based inexact block coordinate descent algorithm is proposed to tackle the formulated non‐convexity problem. For the problem with unit modulus constraint for the intelligent reflecting surface reflection coefficients, closed‐form solutions of the optimisation variables are obtained by utilising the Lagrange multiplier method and the complex circular manifold method. For the problem with intelligent reflecting surface reflection coefficient amplitude phase‐shift dependence, alternate optimisation‐based penalty method is used to obtain the intelligent reflecting surface optimal reflection matrix. Numerical results indicate that the algorithm for the intelligent reflecting surface reflection coefficient unit modulus constraint achieves the maximum secrecy rate, and the algorithm for the intelligent reflecting surface reflection coefficient of amplitude phase‐shift dependence has the sub‐optimal performance, and the benchmark schemes such as no intelligent reflecting surface and intelligent reflecting surface random phase shift strategies have the worst and similar performance.

In this paper, we consider an intelligent reflecting surface (IRS)-assisted downlink cognitive radio (CR) system, in which a secondary access point (SAP) communicates with multiple secondary users (SUs) without affecting multiple primary users (PUs) in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only a single PU and multiple SUs is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes.

The effective combination of physical layer security communication and intelligent reflecting surface (IRS) technology has recently attracted extensive attention to improve the system security. Unlike existing works that mostly focus on single-carrier systems, we consider an IRS-assisted multi-carrier MIMO wireless physical layer security communication system, which consists of a legitimate transmitter, a legitimate receiver, an IRS node and an eavesdropper. With the aim of maximizing the sum secrecy rate, the precoding matrix and IRS reflecting coefficient matrix were jointly optimized under the constraints on the budget of the transmit power and unit modulus of IRS reflecting coefficients. An alternate optimization (AO) based inexact block coordinate descent (IBCD) algorithm was proposed to tackle the non-convexity of the formulated problem, where the Lagrange multiplier method and complex circle manifold (CCM) method were adopted to solve the subproblems and then closed-form solutions were obtained at each iteration. Finally, the simulation results validate the effectiveness of the proposed beamforming schemes.

This paper investigates the cooperative full-duplex device-to-device (D2D) communication underlaying a cellular network, where the cellular user (CU) acts as a full-duplex relay to assist the D2D communication. To simultaneously support D2D relaying and uplink transmission, superposition coding and successive interference cancellation are adopted at the CU and the D2D receiver, respectively. The achievable rate region and joint outage probability are derived to characterize the performance of the considered system. In consideration of the fairness between the cellular uplink and the D2D link, an optimal power allocation scheme is proposed to maximize the minimum achievable rate of them. Besides, by analyzing the upper bound of the joint outage probability, we study a suboptimal power allocation to improve the outage performance. The simulation results confirm the theoretical analysis and the advantages of the proposed power allocation schemes.

This paper proposes novel transmission schemes for a class of interference networks that can achieve new tradeoff regions between the sum of degrees of freedom (sum-DoF) and channel state information (CSI) feedback delay with distributed and temperately-delayed CSI at the transmitter (CSIT). A significant impact of the results is they reveal that distributed and temperately-delayed CSIT contributes to achieve better sum-DoF than that without CSIT in a certain class of interference networks. Specifically, a distributed space-time interference alignment (STIA) scheme is proposed for the two-user multiple-input multiple-output (MIMO) X channel via a novel precoding method called Cyclic Zero-padding. The achieved sum-DoFs herein for certain antenna configurations are greater than the best known sum-DoFs in literature with delayed CSIT. Furthermore, we propose a distributed retrospective interference alignment (RIA) scheme that achieves more than 1 sum-DoF for the K-user single-input single-output (SISO) X network. Finally, we extend the distributed STIA to the M×N user multiple-input single-output (MISO) X network, where each transmitter has N - 1 antennas and each receiver has a single antenna, yielding the same sum-DoF as that in the global and instantaneous CSIT case. The discussion and result of the MISO X network can be extended to the MIMO case due to the spatial scale invariance property.