The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best channel resources with the assistance of self-scrutinized wireless channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of channel observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation (iQRA) mechanism is proposed for MAC layer channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed iQRA mechanism is evaluated in diverse WLANs with throughput, channel access delay, and fairness as performance metrics.

The IEEE 802.11ax high-efficiency wireless local area network (HEW) is promising as a foundation for evolving the fifth-generation (5G) radio access network on unlicensed bands (5G-U). 5G-U is a continued effort toward rich ubiquitous communication infrastructures, promising faster and reliable services for the end user. HEW is likely to provide four times higher network efficiency even in highly dense network deployments. However, the current wireless local area network (WLAN) itself faces huge challenge of efficient radio access due to its contention-based nature. WLAN uses a carrier sense multiple access with collision avoidance (CSMA/CA) procedure in medium access control (MAC) protocols, which is based on a binary exponential backoff (BEB) mechanism. Blind increase and decrease of the contention window in BEB limits the performance of WLAN to a limited number of contenders, thus affecting end-user quality of experience. In this paper, we identify future use cases of HEW proposed for 5G-U networks. We use a self-scrutinized channel observation-based scaled backoff (COSB) mechanism to handle the high-density contention challenges. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is formulated to analyze the performance efficiency of the proposed solution. The analytical and simulation results show that the proposed mechanism can improve user experience in 5G-U networks.

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A channel observation-based scaled backoff (COSB) mechanism for the carrier sense multiple access with collision avoidance of high efficiency wireless local area networks (WLANs) is devised. The proposed protocol modifies the blind scaling of contention window (W) in binary exponential backoff (BEB) scheme of currently deployed WLANs. COSB is employed to adaptively scale-up and scale-down the W size during the backoff mechanism for collided and successfully transmitted data frames, respectively. It can achieve higher throughput and shorter delay compared to the conventional BEB mechanism in highly dense WLANs.

The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.