Resource Allocation and Pricing in Secondary Dynamic Spectrum Access Networks

The paradigm shift from static spectrum allocation to a dynamic one has opened many challenges that need to be addressed for the true vision of Dynamic Spectrum Access (DSA) to materialize. This dissertation proposes novel solutions that include: spectrum allocation, routing, and scheduling in DSA networks. First, we propose an auction-based spectrum allocation scheme in a multi-channel environment where secondary users (SUs) bid to buy channels from primary users (PUs) based on the signal to interference and noise ratio (SINR). The channels are allocated such that i) the SUs get their preferred channels, ii) channels are re-used, and iii) there is no interference. Then, we propose a double auction-based spectrum allocation technique by considering multiple bids from SUs and heterogeneity of channels. We use virtual grouping of conflict-free buyers to transform multi-unit bids to single-unit bids. For routing, we propose a market-based model where the PUs determine the optimal price based on the demand for bandwidth by the SUs. Routes are determined through a series of price evaluations between message senders and forwarders. Also, we consider auction-based routing for two cases where buyers can bid for only one channel or they could bid for a combination of non-substitutable channels. For a centralized DSA, we propose two scheduling algorithms-- the first one focuses on maximizing the throughput and the second one focuses on fairness. We extend the scheduling algorithms to multi-channel environment. Expected throughput for every channel is computed by modelling channel state transitions using a discrete-time Markov chain. The state transition probabilities are calculated which occur at the frame/slot boundaries. All proposed algorithms are validated using simulation experiments with different network settings and their performance are studied

Learning for Cross-layer Resource Allocation in the Framework of Cognitive Wireless Networks

The framework of cognitive wireless networks is expected to endow wireless devices with a cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In this dissertation, we focus on the problem of developing cognitive network control mechanisms without knowing in advance an accurate network model. We study a series of cross-layer resource allocation problems in cognitive wireless networks. Based on model-free learning, optimization and game theory, we propose a framework of self-organized, adaptive strategy learning for wireless devices to (implicitly) build the understanding of the network dynamics through trial-and-error. The work of this dissertation is divided into three parts. In the first part, we investigate a distributed, single-agent decision-making problem for real-time video streaming over a time-varying wireless channel between a single pair of transmitter and receiver. By modeling the joint source-channel resource allocation process for video streaming as a constrained Markov decision process, we propose a reinforcement learning scheme to search for the optimal transmission policy without the need to know in advance the details of network dynamics. In the second part of this work, we extend our study from the single-agent to a multi-agent decision-making scenario, and study the energy-efficient power allocation problems in a two-tier, underlay heterogeneous network and in a self-sustainable green network. For the heterogeneous network, we propose a stochastic learning algorithm based on repeated games to allow individual macro- or femto-users to find a Stackelberg equilibrium without flooding the network with local action information. For the self-sustainable green network, we propose a combinatorial auction mechanism that allows mobile stations to adaptively choose the optimal base station and sub-carrier group for transmission only from local payoff and transmission strategy information. In the third part of this work, we study a cross-layer routing problem in an interweaved Cognitive Radio Network (CRN), where an accurate network model is not available and the secondary users that are distributed within the CRN only have access to local action/utility information. In order to develop a spectrum-aware routing mechanism that is robust against potential insider attackers, we model the uncoordinated interaction between CRN nodes in the dynamic wireless environment as a stochastic game. Through decomposition of the stochastic routing game, we propose two stochastic learning algorithm based on a group of repeated stage games for the secondary users to learn the best-response strategies without the need of information flooding

Blockchain-based distributive auction for relay-assisted secure communications

Physical layer security (PLS) is considered as a promising technique to prevent information eavesdropping in wireless systems. In this context, cooperative relaying has emerged as a robust solution for achieving PLS due to multipath diversity and relatively lower transmission power. However, relays or the relay operators in the practical environment are unwilling for service provisioning unless they are incentivized for their cost of services. Thus, it is required to jointly consider network economics and relay cooperation to improve system efficiency. In this paper, we consider the problem of joint network economics and PLS using cooperative relaying and jamming. Based on the double auction theory, we model the interaction between transmitters seeking for a particular level of secure transmission of information and relay operators for suitable relay and jammer assignment, in a multiple source-destination networks. In addition, theoretical analyses are presented to justify that the proposed auction mechanism satisfies the desirable economic properties of individual rationality, budget balance, and truthfulness. As the participants in the traditional centralized auction framework may take selfish actions or collude with each other, we propose a decentralized and trustless auction framework based on blockchain technology. In particular, we exploit the smart contract feature of blockchain to construct a completely autonomous framework, where all the participants are financially enforced by smart contract terms. The security properties of the proposed framework are also discussed

Cooperative Relaying In Power Line Environment: A Survey and Tutorial

Exchange of information is essential in any society and the demand for faster, cheaper, and secure communications is increasing every day. With other hi-tech initiatives like IPv6 and Internet-of-Things (IOT) already in the horizon, demand for broadband is set to escalate beyond its current level. Inherently laden in the challenges posed by this technology are fresh opportunities in terms of penetration of data services into rural communities and development of innovative strategies for more efficient use of the grid. Though still in its developmental phase/stage, Power Line Communication (PLC) has grown beyond theoretical fantasy to become a reality. The proofs are the readily available PLC systems that can be purchased off the shelfto achieve in-house networking and the much talked about, smart metering technology; generally regarded as the “new bride” in utilities industry. One of the biggest gains of PLC is its use of existing electrical cables, thereby eliminating cost of installation and maintenance of data cables. However, given that the power infrastructure was traditionally built to deliver electricity, data signals do suffer various forms of distortions and impairments as they transit it. This paper presents a tutorial on the deployed wireless system technique which is to be adapted to PLC scenario for the purpose of managing the available source energy for achieving reliable communication system. One of these techniques is the cooperative diversity. Its application and deployment in power line environment is explored. The improvement achieved through cooperative diversity in some PLC systems were presented along with the associated limitations. Finally, future areas of research which will further improve the reliability of PLC systems and reduce its power consumption during transmission is shown

Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

Markets for spectrum were first proposed by Ronald Coase as a way to efficiently allocate this resource. It took another forty years for primary markets to be developed (in the form of spectrum auctions) as the mechanism for assigning spectrum licenses to users. It is not a secret that secondary markets would be necessary to fully realize the benefits of economic allocation of spectrum. But this is easier said than done, since spectrum is a complex, multi-dimensional product with relatively few buyers and sellers (at least for commercial mobile services), so liquid secondary markets have not emerged, even though spectrum trading through brokers is commonplace.\ud \ud In this paper, we find that liquidity for spectrum markets can be improved over "naked" spectrum markets when a standardized commodity can be traded that uses the principles of spectrum virtualization. We utilize the Physical Resource Blocks (PRBs) of LTE-Advanced as the traded commodity and modify the SPECTRAD model developed in [5] accordingly. Though much remains to be done, we find that this is a promising approach to finally realizing liquid secondary markets in radio spectrum