Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Non-convex Quadratically Constrained Quadratic Programming: Hidden Convexity, Scalable Approximation and Applications

University of Minnesota Ph.D. dissertation. September 2017. Major: Electrical Engineering. Advisor: Nicholas Sidiropoulos. 1 computer file (PDF); viii, 85 pages.Quadratically Constrained Quadratic Programming (QCQP) constitutes a class of computationally hard optimization problems that have a broad spectrum of applications in wireless communications, networking, signal processing, power systems, and other areas. The QCQP problem is known to be NP–hard in its general form; only in certain special cases can it be solved to global optimality in polynomial-time. Such cases are said to be convex in a hidden way, and the task of identifying them remains an active area of research. Meanwhile, relatively few methods are known to be effective for general QCQP problems. The prevailing approach of Semidefinite Relaxation (SDR) is computationally expensive, and often fails to work for general non-convex QCQP problems. Other methods based on Successive Convex Approximation (SCA) require initialization from a feasible point, which is NP-hard to compute in general. This dissertation focuses on both of the above mentioned aspects of non-convex QCQP. In the first part of this work, we consider the special case of QCQP with Toeplitz-Hermitian quadratic forms and establish that it possesses hidden convexity, which makes it possible to obtain globally optimal solutions in polynomial-time. The second part of this dissertation introduces a framework for efficiently computing feasible solutions of general quadratic feasibility problems. While an approximation framework known as Feasible Point Pursuit-Successive Convex Approximation (FPP-SCA) was recently proposed for this task, with considerable empirical success, it remains unsuitable for application on large-scale problems. This work is primarily focused on speeding and scaling up these approximation schemes to enable dealing with large-scale problems. For this purpose, we reformulate the feasibility criteria employed by FPP-SCA for minimizing constraint violations in the form of non-smooth, non-convex penalty functions. We demonstrate that by employing judicious approximation of the penalty functions, we obtain problem formulations which are well suited for the application of first-order methods (FOMs). The appeal of using FOMs lies in the fact that they are capable of efficiently exploiting various forms of problem structure while being computationally lightweight. This endows our approximation algorithms the ability to scale well with problem dimension. Specific applications in wireless communications and power grid system optimization considered to illustrate the efficacy of our FOM based approximation schemes. Our experimental results reveal the surprising effectiveness of FOMs for this class of hard optimization problems

Towards realisation of spectrum sharing of cognitive radio networks

Cognitive radio networks (CRN) have emerged as a promising solution to spectrum shortcoming, thanks to Professor Mitola who coined Cognitive Radios. To enable efficient communications, CRNs need to avoid interference to both Primary (licensee) Users (PUs), and among themselves (called self-coexistence). In this thesis, we focus on self-coexistence issues. Very briefly, the problems are categorised into intentional and unintentional interference. Firstly, unintentional interference includes: 1) CRNs administration; 2) Overcrowded CRNs Situation; 3) Missed spectrum detection; 4) Inter-cell Interference (ICI); and 5) Inability to model Secondary Users’ (SUs) activity. In intentional interference there is Primary User Emulation Attack (PUEA). To administer CRN operations (Prob. 1), in our first contribution, we proposed CogMnet, which aims to manage the spectrum sharing of centralised networks. CogMnet divides the country into locations. It then dedicates a real-time database for each location to record CRNs’ utilisations in real time, where each database includes three storage units: Networks locations storage unit; Real-time storage unit; and Historical storage unit. To tackle Prob. 2, our second contribution is CRNAC, a network admission control algorithm that aims to calculate the maximum number of CRNs allowed in any location. CRNAC has been tested and evaluated using MATLAB. To prevent research problems 3, 4, and to tackle research problem (5), our third contribution is RCNC, a new design for an infrastructure-based CRN core. The architecture of RCNC consists of two engines: Monitor and Coordinator Engine (MNCE) and Modified Cognitive Engine (MCE). Comprehensive simulation scenarios using ICS Designer (by ATDI) have validated some of RCNC’s components. In the last contribution, to deter PUEA (the intentional interference type), we developed a PUEA Deterrent (PUED) algorithm capable of detecting PUEAs commission details. PUED must be implemented by a PUEA Identifier Component in the MNCE in RCNC after every spectrum handing off. Therefore, PUED works like a CCTV system. According to criminology, robust CCTV systems have shown a significant prevention of clear visible theft, reducing crime rates by 80%. Therefore, we believe that our algorithm will do the same. Extensive simulations using a Vienna simulator showed the effectiveness of the PUED algorithm in terms of improving CRNs’ performance