Near-Optimal Deviation-Proof Medium Access Control Designs in Wireless Networks

Distributed medium access control (MAC) protocols are essential for the proliferation of low cost, decentralized wireless local area networks (WLANs). Most MAC protocols are designed with the presumption that nodes comply with prescribed rules. However, selfish nodes have natural motives to manipulate protocols in order to improve their own performance. This often degrades the performance of other nodes as well as that of the overall system. In this work, we propose a class of protocols that limit the performance gain which nodes can obtain through selfish manipulation while incurring only a small efficiency loss. The proposed protocols are based on the idea of a review strategy, with which nodes collect signals about the actions of other nodes over a period of time, use a statistical test to infer whether or not other nodes are following the prescribed protocol, and trigger a punishment if a departure from the protocol is perceived. We consider the cases of private and public signals and provide analytical and numerical results to demonstrate the properties of the proposed protocols.Comment: 14 double-column pages, submitted to ACM/IEEE Trans Networkin

Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access

© 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/VTC2023-Spring57618.2023.10200766We investigate multichannel relay assisted non-orthogonal multiple access (NOMA) in slotted ALOHA systems, where each user randomly accesses one of different channel slots and different transmit power for uplink transmissions over two-hop links, to and from the relay. By using multi-agent reinforcement learning, we propose greedy and non-greedy random access methods so that each user can learn its best strategies of random access over multiple relay slots. Random collisions and fading over the relay slots are both considered. The behaviors of relay-aided NOMA-ALOHA strategies are evaluated with the simulation. It is shown that the greedy method outperforms the non-greedy method in terms of average success rate. For deployment of relay, the greedy method benefits in improving transmission reliability under the symmetric relay channels (between the two-hop links) compared to asymmetric channels. Thus, it is interpreted that the proposed greedy method is more promising to the NOMA-ALOHA systems under a symmetric multichannel relay

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Potential games form a class of non-cooperative games where unilateral improvement dynamics are guaranteed to converge in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on Communications, vol. E98-B, no. 9, Sept. 201

Distributed opportunistic scheduling algorithms for wireless communications.

In this thesis, we propose a number of distributed schemes for wireless communications in the cross layer design context, considering an uplink random access network in which multiple users communicate with a common base station. In addition, we perform a comprehensive study on a splitting based multiuser selection algorithm which is simple, effective, and scales with the network size. First, we investigate a reservation-type protocol in a channel aware ALOHA system. Various Markovian models are used to describe the system and to capture the temporal correlation of the channel evolution. The average throughput of the system is obtained using the Markov Analysis technique and we show that the reservation protocol can achieve better performance than the original channel-aware ALOHA by reducing the collision probability. Second, for better resource utilization in the Opportunistic Multichannel ALOHA scheme, we propose a simple extension to the transmission policy that exploits the idle channels. Performance analysis shows that, theoretically, the maximum system throughput can be improved by up to 63% in the asymptotic case. Through numerical results, it can be seen that a significant gain is achieved even when the system consists of a small number of users. Third, we consider a splitting based multiuser selection algorithm in a probabilistic view. Asymptotic analysis leads to a functional equation, similar to that encountered in the analysis of the collision resolution algorithm. Subject to some conditions, the solution of the functional equation can be obtained, which provides the approximations for the expected number of slots and the expected number of transmissions required by the algorithm in a large system. These results shed light on open design problems in choosing parameters for the algorithm when considering the delay and the overhead jointly. A typical example is to optimize the parameters that minimize the weighted sum of these measures of interest

Game-Theoretic Relay Selection and Power Control in Fading Wireless Body Area Networks

The trend towards personalized ubiquitous computing has led to the advent of a new generation of wireless technologies, namely wireless body area networks (WBANs), which connect the wearable devices into the Internet-of-Things. This thesis considers the problems of relay selection and power control in fading WBANs with energy-efficiency and security considerations. The main body of the thesis is formed by two papers. Ideas from probability theory are used, in the first paper, to construct a performance measure signifying the energy efficiency of transmission, while in the second paper, information-theoretic principles are leveraged to characterize the transmission secrecy at the wireless physical layer (PHY). The hypothesis is that exploiting spatial diversity through multi-hop relaying is an effective strategy in a WBAN to combat fading and enhance communication throughput. In order to analytically explore the problems of optimal relay selection and power control, proper tools from game theory are employed. In particular, non-cooperative game-theoretic frameworks are developed to model and analyze the strategic interactions among sensor nodes in a WBAN when seeking to optimize their transmissions in the uplink. Quality-of-service requirements are also incorporated into the game frameworks, in terms of upper bounds on the end-to-end delay and jitter incurred by multi-hop transmission, by borrowing relevant tools from queuing theory. The proposed game frameworks are proved to admit Nash equilibria, and distributed algorithms are devised that converge to stable Nash solutions. The frameworks are then evaluated using numerical simulations in conditions approximating actual deployment of WBANs. Performance behavior trade-offs are investigated in an IEEE 802.15.6-based ultra wideband WBAN considering various scenarios. The frameworks show remarkable promise in improving the energy efficiency and PHY secrecy of transmission, at the expense of an admissible increase in the end-to-end latency

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial