This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive radio networks (CRNs), leveraging AI-driven spectrum sensing and dynamic access, provide a promising solution to improve spectrum utilization. The paper reviews various deep learning (DL)-based spectrum-sensing methods, highlighting their advantages and challenges. It also explores the use of multi-agent reinforcement learning (MARL) for distributed DSA networks, where agents autonomously optimize power allocation (PA) to minimize interference and enhance quality of service. Additionally, the paper discusses the role of machine learning (ML) in predicting spectrum requirements, which is crucial for efficient frequency management in the fifth generation (5G) networks and beyond. Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. The review also introduces the potential of generative AI (GenAI) for demand-planning and network optimization, enhancing spectrum efficiency and energy conservation in wireless networks (WNs). Finally, the paper highlights future research directions, including improving AI-driven network resilience, refining predictive models, and addressing ethical considerations. Overall, AI is poised to transform wireless communication, offering innovative solutions for spectrum management (SM), security, and network performance.

Propagation models are useful tools for communication system planning and optimization of the radio spectrum which is a limited natural resource. An investigation on the suitable propagation model for Ilorin (8°30'N, 4°33'E), a sub-urban area in the Middle-belt of Nigeria is presented here. Experimental data consists of electric field strength obtained through drive test for two television transmitters on 203.25 MHz and 583.15 MHz. Validated predictions for Free space, Okumura and Hata models were compared analytically. Result shows that these empirical models neglect the actual terrain profile for television broadcast in Ilorin. Okumura model stands as the most suitable with optimal values of root mean square error (RMSE) and average relative error of 0.1319 and 0.4447 for 203.25 MHz and 583.15 MHz respectively. Better results can however be obtained if Okumura model is optimized for Ilorin to enhance electric field strength prediction and coverage estimation.

This paper investigates the performance of graph colouring schemes for frequency assignment in Long-Term Evolution (LTE) networks with ultra-dense femtocells. The aim of the study is to minimize interference in such networks while ensuring efficient spectrum use for these femtocells. The three schemes investigated are the conventional greedy graph colouring algorithm, the saturation degree algorithm and our proposed graph-based theory (GBT) algorithm. The process of frequency assignment is similar in the last two except that the proposed GBT partitions the femtocells into independent sets for an efficient frequency re-use. The performance of these three schemes was analyzed through extensive simulations to determine the SINR and network capacity that can be obtained with the deployment of these schemes using the ITU-R P1238-7 path loss model. The outcome of this study showed that with the absence of a dynamic frequency assignment scheme, interference level is increased as the number of femtocell users within a particular coverage is increased, leading to a reduction in the capacity of such networks. Simulation results showed that all three algorithms considered have the ability to allocate frequencies to femtocells and minimize interference in a densely deployed environment, thereby increasing network capacity. The proposed GBT assigned the least sub-band thereby ensuring spectral efficiency while minimizing harmful interference. Results show that the greedy algorithm has a disadvantage of inefficiently assigning sub-bands randomly, while the saturation degree assigns more sub-bands when compared with the GBT scheme.Keywords — Femtocell, graph colouring, frequency assignment, LTE.

Device-to-Device (D2D) communication has been considered a key enabling technology that can facilitate spectrum sharing in 4G and 5G cellular networks. In order to meet the high data rate demands of these new generation cellular networks, this paper considers the optimization of available spectrum resource through dynamic spectrum access. The utilization of continuous-time Markov chain (CTMC) model for efficient spectrum access in D2D-enabled cellular networks is investigated for the purpose of determining the impact of this model on the capacity improvement of cellular networks. The paper considers the use of CTMC model with both queueing and non-queueing cases called 13-Q CTMC and 6-NQ CTMC respectively with the aim of improving the overall capacity of the cellular network under a fairness constraint among all users. The proposed strategy consequently ensures that spectrum access for cellular and D2D users is optimally coordinated by designing optimal spectrum access probabilities. Numerical simulations are performed to observe the impact of the proposed Markovian queueing model on spectrum access and consequently on the capacity of D2D-enabled cellular networks. Results showed that the proposed 13-Q CTMC provide a more spectrum-efficient sharing scheme, thereby enabling better network performances and larger capabilities to accommodate more users.

The various advancement in cellular technology has drawn huge attention to green communication by the stakeholders. Base station (BS) sleeping technique has been proposed as a way to reduce energy consumption in cellular networks, however, this may result in service delay for cellular users. In this article, we propose a queueing decision model for BS sleeping technique that maximizes energy-efficient utilization of the BSs in a green communication network while reducing the mean service waiting time for cellular users. The proposed model also ensures an optimal and interference-free resource sharing scheme that effectively enables channel borrowing through BS association in the network, thereby reducing call drop rate and further improving quality of service in the network. Numerical simulations are performed to observe the impact of the proposed model on spectrum access and consequently on the throughput of the green communication network. The article also observed the possible improvement in network delay with the proposed model. Results show that the queueing decision model is more energy-efficient. The model also provides a more spectrum-efficient sharing scheme compared to existing models, thereby enabling better network throughput.