TL;DR
A maximum matching algorithm for channel allocation with a faster convergence rate that divides the entire set of cellular users and D2D groups into overlapping clusters based on channel gains and utilizes the Kuhn-Munkres algorithm for the best channel allocation to the D2D groups within the same cluster.
Dense device-to-device (D2D) communication networks with a huge number of directly communicating devices necessitate an efficient radio resource management to maximize network performance. To this end, we first propose a novel intelligent channel reuse based on deep deterministic policy gradient (DDPG) to maximize efficiency of the radio resource usage. In the real-world, any channel reuse inevitably imposes additional interference requiring to i) allocate properly transmission power at individual reused channels and ii) knowledge of a high number of interference channels among devices. Such practical challenges are addressed in existing literature commonly via deep neural networks (DNNs). However, simple coexistence of multiple naturally sub-optimal machine learning models, such as DNN for channel quality prediction, DNN for transmission power allocation, and DDPG for channel reuse leads to a problem with a propagation of inevitable small errors in the prediction by individual models. Consequently, even a small error in the decision taken by one model can mislead decisions taken by other models. To solve this challenge, we further propose a low-complexity Generalized Coordinated Learning (GCL) allowing a coordination of multiple light-weight machine learning models. The GCL employs feedback loops among machine learning models to jointly optimize multiple radio resource management parameters in a coordinated way with awareness of decisions taken by other models. Simulation results demonstrate that the proposed GCL reaches near-optimal performance even in dense D2D networks and improves sum capacity and ratio of users meeting their required communication capacity by up to 69.6% and 22.1%, respectively, compared to state-of-the-art works.
Comparison shows that, under the evaluated DeepMIMO-based scenarios, the examined assignment-based methods exhibit different trade-offs in throughput, energy-consumption-related performance, bandwidth utilization, and adaptability to varying user demands, offering useful insights for 5G MIMO resource allocation studies.
Power spectrum allocation in Device to Device (D2D) communication using Non-Orthogonal Multiple Access (NOMA) presents a challenging optimization problem due to subchannel pairing, continuous power control, and Successive Interference Cancellation (SIC) ordering. These interdependent parameters result in a mixed-integer, non-convex problem subject to requirement of Quality of Service (QoS) constraints. Existing schemes exhibit limitations, as Deep Q-Networks (DQN) approach restricts from limited action space, leading to suboptimal transmit power allocation and reduced energy efficiency. However, Deep deterministic policy gradient (DDPG) scheme often unstables near SIC threshold. To handle these limitations, this research paper addresses Quantum enhanced DDPG (QDDPG) scheme, which integrates hybrid actor-critic with a feasibility aware projection to enforce SIC and QoS constraints. QDDPG reaches a return of 0.97 in 350 episodes, however DDPG and DQN reach to 0.84 and 0.62, respectively. With 60 D2D pairs, QDDPG attains a sum rate of 9.6 versus 8.7 in DDPG and 7.4 in DQN. Energy efficiency equals 5.8 bits/J at 10 pairs in QDDPG, and 4.7 bits/J and 4.1 bits/J in DDPG and DQN, respectively. These results indicate that the proposed QDDPG shows consistent performance improvements over DDPG and DQN schemes under the considered network conditions.
This paper explores the application of a fractional frequency reuse (FFR) strategy purposed for networks that integrate both D2D and RIS technologies in multicell cellular network scenarios, and demonstrates its effectiveness in improving network reliability and efficiency.
Cognitive radio networks (CRNs) has significant potential for optimizing spectrum usage, but they still face issues, such as high energy consumption, increased transmission delay, and inefficient routing due to unpredictable network performance and spectrum variance. Relatively new developments, including deep reinforcement learning (DRL), allow for simultaneous routing and resource management, but still need substantial number of trial and error interactions with the environment, which can consume energy and lead to a significant convergence time. Maintaining optimal connectivity with both primary users (PUs) and secondary users (SUs) in large scale CRNs following a homogenous Poisson process while optimally utilizing spectrum access remains a challenge. Towards advancing these problems, we propose an energy-aware, cross-layer routing design based on an apprenticeship learning framework. Our multi-stage dynamic adjustment rating (DAR) mechanism allows for effective tuning of transmit power to decrease action space (following a multi-level transition) and reduce energy consumption. The Whale Swarm Optimization Algorithm (WsOA) allows us to provide predictions of link connectivity and path probability to ensure a more reliable routing selection is presented. To ensure faster convergence and a minimal memory footprint, we incorporate Deep Q-learning with a rapidly converging local search method based on permutation-equivariant neural networks for unseen environments in the given network scenarios. The simulation results show that the suggested approach outperforms conventional algorithms like CRQ-routing, PM-DQN, and other DQL methods in terms of throughput (100-70) pt/s, routing delay (5-1) ms, packet delivery ratio (100-70) % and Percentage packet loss (5-1) %.
Integrated sensing, communication, and computation (ISCC) provides a critical enabling platform in supporting the diverse services in the Internet of Vehicles (IoV). However, effective heterogeneous IoV service provisioning relies on both communication-centric and beyond-communication performance metrics, making unified resource allocation challenging. Moreover, competition from concurrent services for limited multi-dimensional resources is intensified in dynamic vehicular environments. In this paper, we investigate the resource allocation problem for concurrent communication and target classification services in an ISCC-enabled IoV system. To solve the problem, we first introduce the value of service (VoS) to unify communication rate and classification accuracy into a common measure that captures the degree of heterogeneous service fulfillment. To reduce the complexity of dynamic problem optimization, we propose a digital twin-assisted proximal policy optimization (DTPPO) algorithm, in which the digital twin exploits both current and historical information to generate predictive information, thereby enhancing policy learning in dynamic environments. Furthermore, we develop a large language model (LLM)-enhanced DTPPO (LLM-DTPPO) algorithm, which leverages the contextual understanding and domain knowledge of LLMs to reshape the reward function and improve resource allocation performance under multi-dimensional resource competition. Simulation results based on real-world vehicle mobility traces demonstrate that the proposed algorithms outperform existing benchmark schemes.