Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Medium Access Control Protocols for Ad-Hoc Wireless Networks: A Survey

Studies of ad hoc wireless networks are a relatively new field gaining more popularity for various new applications. In these networks, the Medium Access Control (MAC) protocols are responsible for coordinating the access from active nodes. These protocols are of significant importance since the wireless communication channel is inherently prone to errors and unique problems such as the hidden-terminal problem, the exposed-terminal problem, and signal fading effects. Although a lot of research has been conducted on MAC protocols, the various issues involved have mostly been presented in isolation of each other. We therefore make an attempt to present a comprehensive survey of major schemes, integrating various related issues and challenges with a view to providing a big-picture outlook to this vast area. We present a classification of MAC protocols and their brief description, based on their operating principles and underlying features. In conclusion, we present a brief summary of key ideas and a general direction for future work

Device-to-Device Communication and Multihop Transmission for Future Cellular Networks

The next generation wireless networks i.e. 5G aim to provide multi-Gbps data traffic, in order to satisfy the increasing demand for high-definition video, among other high data rate services, as well as the exponential growth in mobile subscribers. To achieve this dramatic increase in data rates, current research is focused on improving the capacity of current 4G network standards, based on Long Term Evolution (LTE), before radical changes are exploited which could include acquiring additional/new spectrum. The LTE network has a reuse factor of one; hence neighbouring cells/sectors use the same spectrum, therefore making the cell edge users vulnerable to inter-cell interference. In addition, wireless transmission is commonly hindered by fading and pathloss. In this direction, this thesis focuses on improving the performance of cell edge users in LTE and LTE-Advanced (LTE-A) networks by initially implementing a new Coordinated Multi-Point (CoMP) algorithm to mitigate cell edge user interference. Subsequently Device-to-Device (D2D) communication is investigated as the enabling technology for maximising Resource Block (RB) utilisation in current 4G and emerging 5G networks. It is demonstrated that the application, as an extension to the above, of novel power control algorithms, to reduce the required D2D TX power, and multihop transmission for relaying D2D traffic, can further enhance network performance. To be able to develop the aforementioned technologies and evaluate the performance of new algorithms in emerging network scenarios, a beyond-the-state-of-the-art LTE system-level simulator (SLS) was implemented. The new simulator includes Multiple-Input Multiple-Output (MIMO) antenna functionalities, comprehensive channel models (such as Wireless World initiative New Radio II i.e. WINNER II) and adaptive modulation and coding schemes to accurately emulate the LTE and LTE-A network standards. Additionally, a novel interference modelling scheme using the ‘wrap around’ technique was proposed and implemented that maintained the topology of flat surfaced maps, allowing for use with cell planning tools while obtaining accurate and timely results in the SLS compared to the few existing platforms. For the proposed CoMP algorithm, the adaptive beamforming technique was employed to reduce interference on the cell edge UEs by applying Coordinated Scheduling (CoSH) between cooperating cells. Simulation results show up to 2-fold improvement in terms of throughput, and also shows SINR gain for the cell edge UEs in the cooperating cells. Furthermore, D2D communication underlaying the LTE network (and future generation of wireless networks) was investigated. The technology exploits the proximity of users in a network to achieve higher data rates with maximum RB utilisation (as the technology reuses the cellular RB simultaneously), while taking some load off the Evolved Node B (eNB) i.e. by direct communication between User Equipment (UE). Simulation results show that the proximity and transmission power of D2D transmission yields high performance gains for a D2D receiver, which was demonstrated to be better than that of cellular UEs with better channel conditions or in close proximity to the eNB in the network. The impact of interference from the simultaneous transmission however impedes the achievable data rates of cellular UEs in the network, especially at the cell edge. Thus, a power control algorithm was proposed to mitigate the impact of interference in the hybrid network (network consisting of both cellular and D2D UEs). It was implemented by setting a minimum SINR threshold so that the cellular UEs achieve a minimum performance, and equally a maximum SINR threshold to establish fairness for the D2D transmission as well. Simulation results show an increase in the cell edge throughput and notable improvement in the overall SINR distribution of UEs in the hybrid network. Additionally, multihop transmission for D2D UEs was investigated in the hybrid network: traditionally, the scheme is implemented to relay cellular traffic in a homogenous network. Contrary to most current studies where D2D UEs are employed to relay cellular traffic, the use of idle nodes to relay D2D traffic was implemented uniquely in this thesis. Simulation results show improvement in D2D receiver throughput with multihop transmission, which was significantly better than that of the same UEs performance with equivalent distance between the D2D pair when using single hop transmission

Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

A fast and reliable broadcast service for LTE-advanced exploiting multihop device-to-device transmissions

Several applications, from the Internet of Things for smart cities to those for vehicular networks, need fast and reliable proximity-based broadcast communications, i.e., the ability to reach all peers in a geographical neighborhood around the originator of a message, as well as ubiquitous connectivity. In this paper, we point out the inherent limitations of the LTE (Long-Term Evolution) cellular network, which make it difficult, if possible at all, to engineer such a service using traditional infrastructure-based communications. We argue, instead, that network-controlled device-to-device (D2D) communications, relayed in a multihop fashion, can efficiently support this service. To substantiate the above claim, we design a proximity-based broadcast service which exploits multihop D2D. We discuss the relevant issues both at the UE (User Equipment), which has to run applications, and within the network (i.e., at the eNodeBs), where suitable resource allocation schemes have to be enforced. We evaluate the performance of a multihop D2D broadcasting using system-level simulations, and demonstrate that it is fast, reliable and economical from a resource consumption standpoint

Cross layer routing and scheduling for multi-channel Wimax mesh networks

Broadband wireless networks are becoming increasingly popular due to their fast and inexpensive deployment and their capabilities of providing flexible and ubiquitous Internet access. Due to the limitation of shared resources in wireless mesh network such as bandwidth, spatial reuse is introduced for concurrent transmissions. The simultaneous transmissions face many challenges regarding interference on the ongoing transmission. To maximize the network performance of mesh networks in terms of spatial reuse, it is essential to consider a cross-layer for resource allocation in different layers such as the routing network layer, the scheduling resource allocation Media Access Control (MAC) layer and physical layer. Therefore, this thesis focuses on improving the spatial reuse for resource allocation mechanism including routing tree construction by taking into consideration the reliable path, channel assignment and scheduling algorithms. Firstly, a Fuzzy based Constructed Routing Tree (FLCRT) is proposed to incorporate fuzzy logic with routing to enable cognitive capability in packet forwarding for uplink or downlink communication. Secondly, the link-aware routing path is proposed to satisfy the connection lifetime and better routing stability for successful requirements of transmission using multi sponsor node technique. Then, a better understanding of reliability analysis is pursued in the context of homogeneous wireless network. Ultimately, heuristic resource allocation including channel assignment and centralized scheduling algorithms are proposed based on the cellular learning automata to enhance the number of concurrent transmissions in the network by efficiently reusing the spectrum spatially. The attempt of heuristic resource allocation algorithms is to find the maximal number of nodes that could transmit data concurrently. The numerical and simulation results show that FLCRT, Learning Automata Heuristic Channel Assignment (LAHCA), and Learning Automata Heuristic Centralized Scheduling (LAHCS) perform better in terms of scheduling length, channel utilization ratio, and average transmission delay as compared with the existing approaches. The proposed FLCRT scheme with respect to the number of subscriber station (SS) nodes performs better in decreasing the scheduling length, average transmission delay, and channel utilization ratio by 38%, 19%, and 38% compared with Interference-Load-Aware routing. LAHCA algorithm improves the number of channels in comparison with random selection algorithm by 8%. LAHCS algorithm using multi channels proposed by LAHCA can reduce the scheduling time, average transmission delay as well as enhance channel utilization ratio versus number of SS nodes by 7%, 8%, and 6% respectively compared with Nearest algorithm in higher traffic demands