Kazi Ishfaq Ahmed

Kazi Ishfaq Ahmed, Ekram Hossain

Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/ near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this paper, we propose a novel centralized DRL based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, we apply a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. For benchmarking the proposed approach, we use a Genetic Algorithm (GA) to obtain near-optimal power allocation solution. Simulation results show that the proposed DRL-based power allocation scheme performs better compared to the conventional power allocation schemes in a multi-cell scenario.

K. I. Ahmed, H. Tabassum, E. Hossain

Increased complexity and heterogeneity of emerging 5G and beyond 5G (B5G) wireless networks will require a paradigm shift from traditional resource allocation mechanisms. Deep learning (DL) is a powerful tool where a multi-layer neural network can be trained to model a resource management algorithm using network data.Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems. In this context, this article focuses on the application of DL to obtain solutions for the radio resource allocation problems in multi-cell networks. Starting with a brief overview of a deep neural network (DNN) as a DL model, relevant DNN architectures and the data training procedure, we provide an overview of existing state-of-the-art applying DL in the context of radio resource allocation. A qualitative comparison is provided in terms of their objectives, inputs/outputs, learning and data training methods. Then, we present a supervised DL model to solve the sub-band and power allocation problem in a multi-cell network. Using the data generated by a genetic algorithm, we first train the model and then test the accuracy of the proposed model in predicting the resource allocation solutions. Simulation results show that the trained DL model is able to provide the desired optimal solution 86.3% of time.