Ningzhe Shi

In multi-user networks, user fairness is a crucial performance metric alongside overall network efficiency. However, optimizing network management and resource allocation with a focus on instantaneous fairness may lead to degradation in overall performance. Due to the dynamic nature of user channels and the inherently long-term characteristics of user services, incorporating the temporal dimension may improve user fairness with less overall performance degradation. In this paper, a long-term optimization problem is formulated to maximize the α-fair utility of the long-term average rate (αFU-LTAR) with outage probability constraints caused by imperfect (estimated) channel state information (CSI). To achieve long-term fairness for arbitrary α through real-time resource allocation decisions, we design an adaptive fairness mechanism (AFM) that transforms the long-term optimization problem into a series of single-slot optimization (SSO) problems. To solve these SSO problems, a reward-adaptive multi-agent deep reinforcement learning (RA-MADRL) framework is proposed, where an adaptive reward mechanism is designed to adjust each agent’s reward in the current time slot based on its historical service information. Compared to the instantaneous fairness algorithm, the proposed RA-MADRL framework improves fairness (i.e., αFU-LTAR) by more than 13% while increasing the overall network service rate by more than 10%.

This paper investigates an uplink multi-user multiple-input multiple-output (MU-MIMO) system assisted by multiple active reconfigurable intelligent surfaces (ARISs). To analytically characterize the performance of multi-ARIS-assisted MIMO system under practical channel conditions, the operator-valued free probability theory is employed to derive closed-form expressions for the ergodic sum rate in spatially transceiver-correlated MIMO channels. Building on these expressions, an alternating optimization framework is developed to jointly design the transmit covariance matrices of the users and the phase-shift matrices of the ARISs, with the objective of maximizing the system energy efficiency (EE). The inherent non-convexity of the formulated problem is addressed by decomposing it into tractable subproblems, which are solved via the Dinkelbach algorithm and a gradient descent method. Simulation results verify the accuracy of the theoretical analysis and demonstrate that the proposed design achieves substantial EE gains and rapid convergence across a wide range of system configurations.

Mobile Edge Computing (MEC) is promising to enable low delay services with which users can offload computing intensive and delay sensitive tasks to the edge. Considering a multi-cell MEC (MC-MEC) network without sufficient resources to serve all users, user selection and non-orthogonal multiple access (NOMA) should be introduced. Then, to maximize the delay-aware average user service satisfaction degree (DA-AveUSD), user selection and resource allocation are jointly optimized (DA-JUSRA), which is modeled as a mixed integer nonlinear programming (MINLP) problem and proven to be NP-hard. To solve this problem, it is decomposed into two independent subproblems, i.e., the power allocation (PA) problem and the user selection, subchannel scheduling and computing resource allocation (USC) problem. Next, a convex evolutionary alternating optimization (CEAO) algorithm is proposed, which alternately applies the convex optimization method and the Karush-Kuhn-Tucker (KKT)-embedding enhanced elite genetic algorithm KKT-embedding E2GA to solve the PA and the USC problem, respectively. Simulations show that compared to the optimal exhaustive search algorithm, the proposed CEAO algorithm converges rapidly within a few iterations, with a gap in DA-AveUSD of less than 1% to the optimum performance. Next, compared to existing user selection schemes, DA-JUSRA with CEAO can enhance DA-AveUSD by more than 50% and yield a higher optimal load.

Information security and privacy are critical for unmanned aerial vehicles (UAV) based low-altitude networks. To enhance the security of the UAV transmitter, this paper inves tigates the jammer-assisted UAV covert communication systems with imperfect CSI, where part of friendly jammers is selected to send artificial noise (AN) to confuse the warden. Then, the UAV transmitter can hide the transmitted signals into “noise”, decreasing warden's detection probability for the transmission behavior. It is important to jointly optimize the jammer selection and jamming power, as AN sent by friendly jammers will interfere with the warden and receiver, simultaneously. However, existing channel-aware schemes work with a perfect channel state information (CSI) of the transmitter-to-receiver (i.e., covert link) and jammer-to-receiver links, which is impractical in low-altitude networks. Therefore, this paper concerns the imperfect CSI of the covert link and the channel distribution information (CDI) of the UAV transmitter-to-warden link and jamming links from the jammers to the warden and receiver. First, given the imperfect CSI, the warden's average detection error probability (WADEP) is analyzed. Then, given that WADEP is higher than the covert ness constraint, the covert throughput is analytically derived and maximized by jointly optimizing the jammer selection, jamming power, and transmission rate (J-JamSPR-ImCSI). Since the optimization problem is NP-hard, a two-step Jensen's inequality based algorithm is proposed to provide a heuristic solution (2 Step-JIHA). The main idea is to first jointly optimize jammer selection and jamming power with the covertness constraint to minimize the average received AN power at the receiver (which negatively impacts the covert throughput), then a bisection method can be employed to obtain the optimal transmission rate maximizing the covert throughput. Numerical results show that the proposed 2-Step-JIHA can provide a near optimal covert throughput with a lower complexity. Moreover, when the CSI is imperfect, compared to the existing channel-aware scheme, the proposed J-JamSPR-ImCSI with 2-Step-JIHA can improve the covert throughput by 54.26%, when the covertness constraint is 0.9.

Integrated sensing and communication (ISAC) can enhance artificial intelligence-generated content (AIGC) networks by providing efficient sensing and transmission. Existing AIGC services usually assume that the accuracy of the generated content can be ensured, given accurate input data (e.g., pose image) and command (i.e., prompt), thus only the content generation quality (CGQ) is concerned. However, it is not applicable in ISAC-based AIGC networks, where content generation is based on inaccurate sensed data. Moreover, the AIGC model itself introduces generation errors, which depend on the number of generating steps (i.e., computing resources). Thus, to assess the quality of experience (QoE) of ISAC-based AIGC services, this paper proposes a content accuracy and quality aware service assessment metric (CAQA). Since allocating more resources to sensing and generating improves content accuracy but may reduce communication quality, and vice versa, this sensing-generating (computing)-communication three-dimensional resource tradeoff must be optimized to maximize the average CAQA (AvgCAQA) across all users with AIGC (CAQA-AIGC). This problem is NP-hard, with a large solution space that grows exponentially with the number of users. To solve the CAQA-AIGC problem with low complexity, a standard linear programming (LP) guided deep reinforcement learning (DRL) algorithm with an action filter (LPDRL-F) is proposed. Through the LP-guided approach and the action filter, LPDRL-F can transform the original three-dimensional solution space to two dimensions, reducing complexity while improving the learning performance of DRL. Simulations show that compared to existing DRL and generative diffusion model (GDM) algorithms without LP, LPDRL-F converges faster and finds better resource allocation solutions, thus improving AvgCAQA by more than 10%. With LPDRL-F, CAQA-AIGC can achieve an improvement in AvgCAQA of more than 50% compared to existing schemes focusing solely on CGQ.

In integrated sensing and communication (ISAC) enabled unmanned aerial vehicle (UAV) systems, based on sensed information such as user positions, UAV scheduling could be optimized to enhance the communication performance. However, sensing errors are inevitable, leading to a performance degradation. This paper proposes a sensing-error-aware (SEA) multi-UAV scheduling scheme (SEA-scheduling). First, the impact of the sensing errors on communication performance is analyzed, and a SEA communication rate is derived. Then, targeting to maximize this SEA rate, multi-UAV collaborative scheduling is jointly optimized with sensing resource allocation. The problem is solved by decomposing into two subproblems, i.e., a joint UAV position schedule, user association and bandwidth allocation optimization subproblem (PUB) and a sensing resource optimization subproblem (SRO), which can be solved iteratively. A generative diffusion(GD)-driven multi-agent reinforcement learning (GD-MADRL) algorithm is proposed to solve PUB, and a classical simulated annealing (SA) algorithm is adopted to solve SRO. The main idea of GD-MADRL is to introduce the GD model in MADRL to generate training data with sensing errors, enhancing the robustness of generated UAV scheduling strategies. Simulation results demonstrate that when there are sensing errors, the proposed SEA-scheduling scheme improves the communication rate by up to 30% compared to existing sensing-error-unaware schemes.

To facilitate the popularization and application of semantic communication, it is crucial to ensure its efficient coexistence with traditional digital communication networks. In this paper, we investigate a non-orthogonal multiple access (NOMA)-enabled heterogeneous semantic communication (SemCom) and bit communication (BitCom) coexistence system under an orthogonal frequency division multiplexing (OFDM) framework. The deployment of fluid antennas at the user side is treated as a crucial strategy to enhance system efficiency and mitigate interference. Within the above OFDM framework, we construct a frequency domain channel with multicarriers, based on the multipath channel characteristics exhibited by different fluid antenna ports. Subsequently, a sum semantic rate maximization problem is formulated by optimizing the subcarrier allocation, power allocation, and the fluid antenna port selection of the users. Note that the derived problem is challenging to solve through conventional methods due to the presence of highly coupled continuous and discrete variables. To address this challenge, we propose a dual proximal policy optimization (DPPO) algorithm as a solution for this problem, which divides the problem into two parts and leverages separate subnetworks to solve each part. Simulation results demonstrate the efficiency of the proposed algorithms in enhancing the sum semantic rate.

Centralized open radio access networks (O-RANs) aggregate computing resources of multiple cells to improve resource utilization and achieve statistical multiplexing gain (SMG), i.e., the ratio of the number of deployed resources (DepR) for distributed radio access network (D-RAN) to that for O-RAN. SMG of O-RAN has been investigated with infinite number of cells with homogeneous bursty-temporal traffic features. However, in practice, the number of cells is limited, and in the era of Internet of Things (IoT), cells with heterogeneous bursty-temporal traffics (het-traffics), should be considered. This paper analyzes the SMG of O-RAN with limited het-traffic cells. First of all, given traffic models of het-traffic cells following different bursty lognormal distribution and periodic time-varying features, required resources (ReqR) for D-RAN can be derived. Then, to obtain the distribution of ReqR for O-RAN with limited het-traffic cells, a Gaussian mixture model (GMM) based resource distribution approximation (GMM-RDA) is proposed. Thus SMG can be obtained for any given service rate. In simulations, the effectiveness of GMM-RDA is verified by Kolmogorov-Smirnov (K-S) test, and the accuracy of achieved SMG can be up to 99.7%, which is about 33% higher than that obtained by the law of large numbers (LLN) for infinite cells. Moreover, het-traffic cells resulting from service diversification can enhance SMG in O-RAN by 34.7%.

Task-oriented communications with semantic encoders are promising to enhance the communication efficiency, by selecting and transmitting valuable data according to task requirements/queries. However, existing semantic encoders lack the capability to track the changing in queries, leading to biased data selection. This paper proposes a query-aware semantic encoder, i.e., Query-Data Cross (QDC) encoder for task-oriented communications. By consistently focusing on data features that are most relevant to the current query at the transmitter, QDC can adapt to changing queries. Based on the dynamic semantic relevance obtained by QDC, a relevance-based data selection and bandwidth allocation optimization (RDSBA) problem is formulated, considering a multi-device task-oriented communication system, where devices should transmit valuable data with high relevance to the queries broadcasted by the base station (BS). RDSBA aims to maximize the data profit of all devices, which is defined as the difference between the relevance of data selected for the BS and the cost of obtaining the data. Then, a DRL-based data selection and bandwidth allocation (DRL-DB) algorithm is proposed to solve the NP-hard optimization problem. Simulation results demonstrate that QDC can smartly track the changing in queries and achieve an accuracy of at least 85% in relevance evaluation, more than 8% higher than existing schemes. Based on the relevance provided by QDC, the proposed RDSBA scheme with DRL-DB can increase the data profit by at least 18%, comparing to existing schemes.