Hybrid optimization-based deep learning for energy efficiency resource allocation in MIMO-enabled wireless networks.

Resource allocation in multiple-input multiple-output (MIMO)-enabled wireless networks is designated for multiple users, which aims to optimize the distribution of network resources. This network's main intent is to maximize system performance by improving energy efficiency. However, the users of MIMO need many resources for effective operation. Hence, deep learning (DL) techniques are developed in this 5G network field to attain better reliability and accuracy during resource allocation. Therefore, this paper introduces a hippo graylag goose optimization with XCovNet (HGGO_XCovNet) for resource allocation. Firstly, a base station (BS) with multiple users is considered and the resource allocation is carried out by considering various objective functions, namely signal-interference noise ratio (SINR), data rate, and power consumption. Moreover, the resource allocation is performed by employing a DL model called XCovNet, where Xception convolutional neural network (XCovNet) is trained using the proposed hippo graylag goose optimization (HGGO). Further, the HGGO is formulated by the combination of greylag goose optimization (GGO) and hippopotamus optimization algorithm (HO). Furthermore, the HGGO_XCovNet technique measured a maximum energy efficiency of 74.943 kbits/joule, a sum rate of 269.93 Mbps, and throughput of 551.262 Mbps.