# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
###### Abstract  
Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput and lower power consumption. Firstly, maximizing the throughput and minimizing power consumption are formulated as a constrained optimization problem. Then, the obtained solution, alongside a heuristic solution, is compared against the solutions to existing approaches, showing our proposed strategy is drastically superior in terms of both power efficiency and system throughput. This study is then concluded by employing artificial intelligence techniques in multi-objective optimization, namely random forest regression, particle swarm, and genetic algorithms, to balance out the trade-offs between maximizing the throughput and power efficiency and minimizing energy consumption. These investigations demonstrate the potential of employing the proposed LTE-LAA architecture in addressing the requirements of next-generation HetNets in terms of power, throughput, and green scalability. AI optimization; cognitive radio; software-defined radio; green communication +
Footnote †: journal: Sensors  
HetNets have the potential to effectively address major issues related to spectrum scarcity and energy consumption in next-generation networks. In HetNets, a diverse array of base stations (BSs), including femto, pico, and micro BSs, collectively known as small-cell base stations (SCBSs), are strategically deployed, aiming to alleviate the overall power consumption and spectrum requirements. These small-cell BSs vary in terms of their coverage area and transmission power, allowing HetNets to enhance their capacity by employing an intensive frequency reuse approach within contiguous cells, thereby mitigating spectrum scarcity issues. Both network operators and users stand to benefit from this approach. From the users' perspective, HetNets bring network infrastructure closer, leading to improved link reliability and quality. However, managing high-density networks like HetNets, which heavily rely on frequency reuse, presents a challenging task due to the tight coordination among neighboring base stations. This coordination manifests in several ways. Firstly, because of the small cell coverage, user mobility causes rapid fluctuations in network load. Therefore, tasks such as resource allocation, cell association, and handovers must be synchronized among all BSs, including contiguous ones, to minimize signal interference, boost network capacity, balance traffic loads [5], and minimize network energy consumption [6; 7]. Secondly, the extensive frequency reuse and broadcast nature of wireless communication result in significant interference experienced by users of one BS from surrounding BSs. This interference negatively impacts the usable capacity of HetNets [5]. Software-defined radio (SDR) technology, which can dynamically update the operating parameters, including the frequency bands, can be quite useful in this context [8]. SDR offers an opportunity for HetNets to adapt to changed network conditions and transcends traditional hardware constraints by replacing fixed hardware components with programmable software algorithms, enabling dynamic signal processing functionalities such as modulation, demodulation, and filtering. This flexibility allows SDRs to support multiple communication standards and protocols through software-defined implementations, eliminating the need for separate hardware for each standard and facilitating easy upgrades as standards evolve.