Distributed Adaptive Algorithms for Optimal Opportunistic Medium Access

We examine threshold-based transmission strategies for distributed opportunistic medium access in a scenario with fairly general probabilistic interference conditions. Specifically, collisions between concurrent transmissions are governed by arbitrary probabilities, allowing for a form of channel capture and covering binary interference constraints as an important special case. We address the problem of setting the threshold values so as to optimize the aggregate throughput utility of the various users, and particularly focus on a weighted logarithmic throughput utility function (Proportional Fairness). We provide an adaptive algorithm for finding the optimal threshold values in a distributed fashion, and rigorously establish the convergence of the proposed algorithm under mild statistical assumptions. Moreover, we discuss how the algorithm may be adapted to achieve packet-level stability with only limited exchange of queue length information among the various users. We also conduct extensive numerical experiments to corroborate the theoretical convergence results.14 page(s

Distributed SIR-Aware Opportunistic Access Control for D2D Underlaid Cellular Networks

In this paper, we propose a distributed interference and channel-aware opportunistic access control technique for D2D underlaid cellular networks, in which each potential D2D link is active whenever its estimated signal-to-interference ratio (SIR) is above a predetermined threshold so as to maximize the D2D area spectral efficiency. The objective of our SIR-aware opportunistic access scheme is to provide sufficient coverage probability and to increase the aggregate rate of D2D links by harnessing interference caused by dense underlaid D2D users using an adaptive decision activation threshold. We determine the optimum D2D activation probability and threshold, building on analytical expressions for the coverage probabilities and area spectral efficiency of D2D links derived using stochastic geometry. Specifically, we provide two expressions for the optimal SIR threshold, which can be applied in a decentralized way on each D2D link, so as to maximize the D2D area spectral efficiency derived using the unconditional and conditional D2D success probability respectively. Simulation results in different network settings show the performance gains of both SIR-aware threshold scheduling methods in terms of D2D link coverage probability, area spectral efficiency, and average sum rate compared to existing channel-aware access schemes.Comment: 6 pages, 6 figures, to be presented at IEEE GLOBECOM 201

Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201

SDDV: scalable data dissemination in vehicular ad hoc networks

An important challenge in the domain of vehicular ad hoc networks (VANET) is the scalability of data dissemination. Under dense traffic conditions, the large number of communicating vehicles can easily result in a congested wireless channel. In that situation, delays and packet losses increase to a level where the VANET cannot be applied for road safety applications anymore. This paper introduces scalable data dissemination in vehicular ad hoc networks (SDDV), a holistic solution to this problem. It is composed of several techniques spread across the different layers of the protocol stack. Simulation results are presented that illustrate the severity of the scalability problem when applying common state-of-the-art techniques and parameters. Starting from such a baseline solution, optimization techniques are gradually added to SDDV until the scalability problem is entirely solved. Besides the performance evaluation based on simulations, the paper ends with an evaluation of the final SDDV configuration on real hardware. Experiments including 110 nodes are performed on the iMinds w-iLab.t wireless lab. The results of these experiments confirm the results obtained in the corresponding simulations

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented