SWARM INTELLIGENCE FOR NEXT-GENERATION WIRELESS NETWORKS: RECENT ADVANCES AND APPLICATIONS 1 Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications Quoc-Viet Pham, Dinh C. Nguyen, Seyedali Mirjalili, Dinh Thai Hoang, Diep N. Nguyen, Pubudu N. Pathirana, and Won-Joo Hwang Abstract—Due to the proliferation of smart devices and emerg- complete this survey (2020 August), there have been some ing applications, many next-generation technologies have been 5G commercial deployments based on the New Radio (NR) paid for the development of wireless networks. Even though standard [2]. The pace of deployment would be accelerated by commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial the completion of 3rd Generation Partnership Project (3GPP) communities for 6G systems. In such a network, a very large Release 16 specification with more details and enhancements number of devices and applications are emerged, along with concerning industrial Internet of Things (IoT), satellite in- heterogeneity of technologies, architectures, mobile data, etc., tegration, NR-based unlicensed spectrum, enhanced multi- and optimizing such a network is of utmost importance. Besides input multi-output (eMIMO), advanced vehicle to everything convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless (V2X) support, ultra-reliable and low latency communication networks. As a new subdivision of artificial intelligence, SI is (uRLLC) enhancements, and security [3]. In summary, three inspired by the collective behaviors of societies of biological main use cases supported in 5G wireless systems are enhanced species. In SI, simple agents with limited capabilities would mobile broadband (eMBB), massive IoT, and uRLLC. achieve intelligent strategies for high-dimensional and challenging Besides many benefits offered by NGN (e.g., higher data problems, so it has recently found many applications in next- generation wireless networks (NGN). However, researchers may rates and reliability, lower latency, and more capacity), there not be completely aware of the full potential of SI techniques. are many challenges needed to be tackled to make it successful In this work, our primary focus will be the integration of these (e.g., the network operators need to reduce their expenditure two domains: NGN and SI. Firstly, we provide an overview of SI and complexity of operations, and how to support more use techniques from fundamental concepts to well-known optimizers. cases and services from the third-party companies). There Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource are many problems needed to be studied in NGN such as allocation, wireless caching and edge computing, network secu- joint power control and user clustering in non-orthogonal rity, and several other miscellaneous issues. Finally, we highlight multiple access (NOMA) networks [4], task offloading and open challenges and issues in the literature, and introduce some resource allocation in multi-access edge computing (MEC) interesting directions for future research. systems [5], pilot allocation in V2X vehicular communications Index Terms—5G and Beyond, 6G, Artificial Intelligence and MIMO [6], network slicing in virtualized networks [7], (AI), Computational Intelligence, Swarm intelligence (SI), Next- and beamforming in (cell-free massive) MIMO systems [8]. Generation Wireless Networks. Several powerful techniques, e.g., convex optimization, game theory, and machine learning have been used to solve these I. INTRODUCTION problems. As a suitable alternative, swarm intelligence (SI), a subset of artificial intelligence (AI), has been recently and Four generations of cellular networks have been intro- widely used in the literature with impressive performance. duced since the inception of the first generation in early Despite promising results, we are not aware of any survey arXiv:2007.15221v1 [cs.NI] 30 Jul 2020 1980s. It is expected that massive numbers of connected dedicated to the use of SI techniques for emerging issues in devices, applications, technologies, network architecture, etc. NGN. Filling this gap, we carry out a contemporary survey will be available in next-generation wireless networks (NGN, on the applications of swarm intelligence (SI) techniques for i.e., fifth-generation (5G) and beyond) [1]. At the time we NGN. The investigation on emerging issues includes spectrum management and resource allocation, wireless caching and Quoc-Viet Pham is with Research Insitute of Computer, Information and Communication, Pusan National University, 2, Busandaehak-ro 63beon-gil, edge computing, network security, and miscellaneous issues. Geumjeong-gu, Busan 46241, Korea (e-mail: [email protected]). Dinh C. Nguyen and Pubudu N. Pathirana are with School of Engineering, Deakin University, Waurn Ponds, VIC 3216, Australia (e-mails: cdnguyen, A. A Brief On Optimization Tools [email protected]). Seyedali Mirjalili is with Center for Artificial Intelligence Research and Op- Various methods have been applied to address emerging is- timization, Torrens University Australia, 90 Bowen Terrace, Fortitude Valley, sues in NGN. We briefly describe these techniques as follows. Brisbane, QLD 4006, Australia (e-mail: seyedali.mirjalili@griffithuni.edu.au). Dinh Thai Hoang and Diep N. Nguyen are with School of Electrical and • Convex Optimization: In the event that an optimization Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, problem is convex, some well-know methods and off- Australia (e-mail: fdiep.nguyen, [email protected]). the-shelf solvers, for example, interior-point method and Won-Joo Hwang is with School of Biomedical Convergence Engineering, Pusan National University, 49, Busandaehak-ro, Mulgeum-eup, Yangsan-si, CVX [9] would be used to get the optimal solution. Gyeongsangnam-do 50612, Korea (e-mail: [email protected]). The convexity feature is rarely available and scholars 2 SWARM INTELLIGENCE FOR NEXT-GENERATION WIRELESS NETWORKS: RECENT ADVANCES AND APPLICATIONS typically utilize some approaches to convexify the un- neural networks with a very large dataset and massive derlying problem or approximate it as a sequence of optimization parameters is time-consuming and usually convex problems, e.g., difference of two convex functions transferred to off-line training [21]. (DC) programming [10], semidefinite relaxation (SDR) Other tools for modeling and analyzing wireless networks can [11], and second-order cone programming (SOCP) [12]. be stochastic geometry and random graphs, which have been Nevertheless, typically the computational complexity in- found in problems such as interference characterization and creases at an exponential rate with the optimization outage probability, throughput, and packet error rate. However, dimensionality, e.g., the numbers of IoT devices and the from an optimization perspective, these tools are not covered quantization levels of channel states. In such a case, in this subsection. Interested readers are suggested to read the heuristic algorithms are highly preferred thanks to their tutorial [22] and the book [23]. features of simplicity and low complexity. For example, the heuristic method can relax a binary variable as a B. Swarm Intelligence for Next-Generation Wireless Networks continuous one, and similarly a binary constraint as a quadratic one. However, the main drawback of such The main challenge in solving NP-hard problems is an heuristic schemes is that the convergence to optima and exponential increase in the complexity levels (e.g., in time and the existence of the optimality are not guaranteed. memory). In such cases, algorithms that find exact solutions • Game Theory: The purpose of game theory is to study the (i.e., exact algorithms) are not efficient. Especially in NGN, interactions among independent and rational agents, e.g., the massive numbers of connected devices, base stations (BSs), mobile users in a cellular network and edge hosts in MEC channel states, heterogeneous resources, etc. increase the chal- systems. Game theory has a variety of applications in var- lenges of finding the exact algorithms. Simple examples of ious disciplines such as economics, business, philosophy, such problems are mobility-aware user association in ultra- and recently in wireless networks. The design of efficient dense networks, signal detection and channel estimation, and algorithms for large-scale, distributed, dynamic, and het- routing in IoT networks. erogeneous wireless systems, is considered as the primary As a subset of bio-inspired and nature-inspired algorithms, use of game theory. For instance, coalitional game theory SI studies complex collective behavior of the systems com- is used for joint optimization of the network throughput posed of many simple components, which can interact with and content service satisfaction degree in cashing sys- other agents locally and with their surrounding environment tems [13], channel allocation in device-to-device (D2D) [24]. For example, the particle swarm optimization (PSO) is communications [14], and user pairing in NOMA systems designed to mimic the movement of organisms in flocks of [15]. Recently, transport and matching theories, as pow- birds, and the ant colony optimization (ACO) is inspired by erful mathematical frameworks, have been used for many the behavior of ants in