Computationally Intelligent Techniques for Resource Management in MmWave Small Cell Networks

Abstract

Ultra densification in heterogeneous networks (HetNets) and the advent of millimeter wave (mmWave) technology for fifth generation (5G) networks have led the researchers to redesign the existing resource management techniques. A salient feature of this activity is to accentuate the importance of computationally intelligent (CI) resource allocation schemes offering less complexity and overhead. This paper overviews the existing literature on resource management in mmWave-based HetNets with a special emphasis on CI techniques and further proposes frameworks that ensure quality-of-service requirements for all network entities. More specifically, HetNets with mmWavebased small cells pose different challenges as compared to an allmicrowave-based system. Similarly, various modes of small cell access policies and operations of base stations in dual mode, i.e., operating both mmWave and microwave links simultaneously, offer unique challenges to resource allocation. Furthermore, the use of multi-slope path loss models becomes inevitable for analysis owing to irregular cell patterns and blocking characteristics of mmWave communications. This paper amalgamates the unique challenges posed because of the aforementioned recent developments and proposes various CI-based techniques including game theory and optimization routines to perform efficient resource management.

The shift to 6G communications: vision and requirements

“…Therefore, the idea of using an unlicensed spectrum is proposed, i.e., to use the mmWave, THz band, and visible light spectrum, simultaneously [48][49][50][51][52]. These bands are never used for any communication.…”

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

“…Heuristic and iterative algorithms are frequently used in the literature, due to the fact that many optimization problems in this field are non-deterministic polynomial time hard (NP-hard) and thus require decomposition and simplification to create sub-optimal solutions which can then be solved with heuristic algorithms. Game theory and matching theory-based solutions are also attractive for distributed decision-making among entities, as centralized optimization is challenging in multi-stakeholder environments [42][43][44][45][46][47]. Finally, AI-based models (artificial neural networks (ANN) [48,49], Q-learning [50]) are also being utilized to resource management optimization problems.…”

Survey of Radio Resource Management in 5G Heterogeneous Networks

“…Three resource management schemes are proposed in recent works by Munir, et al [100] using CI techniques for different 5G mmWave HetNets. These schemes combine the optimization method and game theory to reduce the overall complexity and overhead signal.…”