Efficient Approximation Algorithms for Link Scheduling With Power Assignment in Wireless Networks Under SINR and Rayleigh-Fading Models

Hierarchical RSMA in Multi-Antenna Downlink Systems: Outage Performance and Power Allocation

Rate-Splitting Multiple Access (RSMA) has emerged as a robust interference management strategy for future wireless networks. This paper investigates the performance of a hierarchical RSMA scheme in the downlink of a multi-antenna system, designed to efficiently serve clustered user deployments. We derive exact and asymptotic closed-form expressions for the outage probability of users under Nakagami- $m$ fading channels, considering a two-layer message splitting architecture (systemcommon, group-common, and private streams). Furthermore, to ensure fairness and reliability, we formulate a min-max power allocation problem to minimize the worst-case outage probability among users. A Geometric Programming-based algorithm is proposed to solve the resulting non-convex optimization problem. The numerical results validate the theoretical analysis and demonstrate the impact of different strategies for using this model, such as the number of users per group, user allocation strategies, and the number of base station transmit antennas.

Adaptive Power Control for Optimal Transmission Power for Throughput Maximization in Wireless Networks

Optimization of Resource Allocation in 5G MIMO Networks Using Linear Assignment Algorithms

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.

Priority-Index Based Policies for Minimizing Average AoI in Multi-Hop Network Convergecast With Complete Interference and Link Losses

We consider convergecast in a multi-hop sensor network, in which sensors sample physical processes of interest and transmit packets containing these samples to a single sink node over a routing tree. The sink node remotely estimates these processes based on received packets. Our objective is to design centralized multi-hop sampling and scheduling policies that minimize the average age of information (AAoI) across sensors for networks operating under a complete interference model as well as random losses in the wireless network. We propose a policy that samples sources and schedules links according to an analytically characterized priority index. Through simulations, we compare the AAoI of the proposed policy with policies from prior work and show improved performance. The improvement in performance is observed to be larger as the number of sources increases or when the reliability of links decreases demonstrating the utility of the proposed policy. An extension to $K$ -hop interference model is also discussed.

Optimization of Transmit Power and Clear Channel Assessment Threshold for Dense Wi-Fi Networks: A Distributed Reinforcement Learning Approach

Wi-Fi, that is wireless networks based on the IEEE 802.11 standard, operates in a decentralized manner based on carrier sense multiple access (CSMA). Owing to the operational characteristics of a CSMA protocol, the effects of interference and channel sensing sensitivity on the overall network throughput and fairness become more significant as the Wi-Fi network gets denser, i.e., the number of access points (APs) increases. The transmit and receive coverage can be adjusted by controlling the transmit power and clear channel assessment (CCA) threshold, respectively; the network performance can then be improved in terms of the network sum throughput and fairness. However, the analytical optimization of the transmit power and CCA threshold is a complicated task because both parameters of multiple APs and stations (STAs) are mutually coupled. Alternatively, the mechanism of the proposed problem is modeled using a Markov decision process (MDP) and the optimal solution is obtained by using a reinforcement learning (RL) approach. Considering the complexity and convergence rate of an algorithm as well as the distributed Wi-Fi network architecture, we propose a distributed multi-agent Q-learning algorithm. The effectiveness of the proposed algorithm is examined through intensive simulations with several benchmarks. Based on the simulation results, it can be deduced that the quality of services (QoS) of dense Wi-Fi networks can be effectively optimized by controlling the transmission power and CCA threshold.

Blockage-Aware Power Allocation Algorithm for Millimeter-Wave Communication with Dynamic Reward Q-Learning

Millimeter-wave (mmWave) communication systems are vulnerable to severe attenuation, blockage-induced LOS/NLOS transitions, and time-varying co-channel interference. This paper develops a lightweight distributed power-allocation framework in which each base station independently updates a tabular Q-learning policy using locally observable blockage-ratio, serving-distance, and aggregate-interference information. The proposed state-dependent dynamic reward is recalculated at every decision step, and its coefficients vary explicitly with the instantaneous blockage ratio, QoS satisfaction ratio, and normalized interference level. All learning-based and non-learning baselines are evaluated using the same topology realizations, blockage and mobility traces, and random seeds. Under the reconstructed simulation settings, the proposed method achieves performance comparable to fixed Q-learning while retaining a transparent blockage-aware state and low-complexity distributed implementation. DQN, greedy, and uniform power achieve higher raw capacity or QoS in the considered small-scale network. Results from 30 paired runs with 95% confidence intervals, together with ablation, sensitivity, beam-misalignment, and overhead analyses, clarify the empirical benefits, limitations, and deployment scope of the proposed method. The study focuses on power control after beam establishment; joint beam tracking and power allocation remain outside the present scope.