Aiming at avoiding interference to licensed primary users, which can help promote the willingness of the primary systems to accept the idea of coexistence with cognitive users, we propose several cumulative-sum (CUSUM)-based algorithms that exploits the feature of the incipient part of the primary signals for detecting as quickly as possible the event that the dormant primary systems start reclaiming the use of the spectrum. Particularly, our formulation captures possible fading effects between the cognitive and the primary user given only the statistical channel information at the receiver end.
Contrast to the homogenous-distributed and independent observations after change that are commonly assumed in conventional quickest detection, the detection problem we deal with involves non-homogenous and innately dependent observations after the reoccupying of the coexisting primary system. To tackle the problem, we first con-sider the single-user scenario and propose four CUSUM-based algorithms depending on different assumptions on the fading environments. Specifically, we call the four pro-posed algorithms as the classical CUSUM, weighted CUSUM, GLRT-based CUSUM, and MMSE-based CUSUM algorithms, which are briefly described as follows. In the classical CUSUM algorithm, we treat the unknown channel factors as random variables with known prior statistics and calculate the likelihood ratio between joint probability density functions of observations under the conditions before and after the reclaiming occurs. While in weighted CUSUM and GLRT-based CUSUM algorithm, we consider the unknown channel coefficient as deterministic but unknown constant during the detec-tion process. We weight the likelihood ratio by applying prior informadetec-tion as weighting function and estimate the unknown parameter through all available observations. The es-timates are then substituted into the likelihood. Depart from the philosophy employed by the GLRT-based CUSUM algorithm, the MMSE-based CUSUM algorithm is to estimate the unknown fading coefficient by incorporating prior knowledge. We also examine the required length of backward observations that keeps comparable efficacy with the one without any curtailment of observational window.
Further, we extend the proposed CUSUM-based algorithms to the case of cooperative quickest detection, where a number of cognitive users provide decision strategies and col-laboratively detects instantaneously the beginning of the reclaims of the primary signal under three different distributed frameworks. The first distributed scenario is in central-ized setting, which means that the original data received at sensors are sent completely to a fusion center where a final decision is made based on all sensor massages for global CUSUM test. In the cases considering decentralized framework, we resort to hard fu-sion of local CUSUM and global CUSUM with quantized local decifu-sion. In hard fufu-sion of local CUSUM scheme, we assume that each of the cooperative sensors has sufficient memory to individually perform CUSUM-based quickest detection then the fusion center
makes final decision based on local decisions sent by sensors according to hard-decision combining rules. In the decentralized scheme considering global CUSUM with quantized local decisions, we propose using an approximation on distributions of the received signal after reoccupying to tackle the quantization at the local sensors, and the CUSUM-based algorithm is performed at the fusion center while the local sensors are assumed memory-less and send quantized version of their observations for decision making.
In the simulations, we demonstrate the effectiveness of the proposed algorithms with the settings defined in IEEE 802.16e as the primary signal model. Comparisons of the proposed CUSUM-based algorithms are also provided in the simulations.
To sum up, the contributions of the research include:
• We deal with the spectrum sensing problem under fading environments in a sequen-tial detection viewpoint, which involves non-homogenous distributed and innately mutually dependent observations after the reoccupying of the coexisting primary system. This problem has not been studied before. Although the work in [25] also discusses quickest spectrum detection with dependent observations after change, the dependency among the observations lies on the sampling of the wideband power spectrum density. They first train the corresponding HMM parameters of specific primary signal and then perform quickest pattern cognition, which heavily depends on the Markov properties in calculating statistics, to detect the appearance of a predefined pattern as quickly as possible. While in our proposed schemes, the de-pendency among observation sequence is due to possible frequency selective fading effects.
• We develop several effective change detection strategies based on CUSUM proce-dure with practical assumptions. In addition, we also propose cooperative schemes as further extension. By simulation, we demonstrate the effectiveness of the pro-posed algorithms either under flat fading or frequency-selective fading case.
Chapter 2
Cognitive Radio and CUSUM-Based Quickest Detection Preliminary
2.1 Cognitive Radio
Cognitive radio technology, which is first proposed by Mitola in [2], has emerged as a potential candidate to revolutionize spectrum utilization. In general, cognitive radio is defined as a software-defined radio that is aware of its surrounding and autonomously adapting its operations to achieve desired objectives in response to unexpected variations, based on the active monitoring of several factors in the external and internal radio envi-ronment, such as radio frequency spectrum, user behavior and network state. The need for CRs is motivated by various factors. Early works focus on the capability of enhancing the flexibility of personal services in a way that supports automated reasoning about the needs of the anticipated user. The radio seeks out the required information and provides the user with instructions or the desired service. Fig. 2.11 illustrates the cognition cycle which consists of Observe, Orient, Plan, Decide, Learn and Act phases, has been widely used to understand and analyze the performance of cognitive processes in cognitive radios and cognitive networks. More recently, the problem of spectrum under-utilization urges the need for intelligent radios to tackle the dynamic allocations efficiently. Although the
1This figure is adapted From Mitola, ”Cognitive Radio: An Integrated Agent Architecture for Soft-ware Defined Radio”, Doctor of Technology, Royal Inst. Technol. (KTH), 2000, pp 48
Figure 2.1: Simplified Cognition Cycle.
initial aim of CR not directly lies on promoting the utilization of spectrum resource, it does serves as a potential candidate to alleviate this problem since cognitive users could either opportunistically utilize idled spectrum by detecting the spectrum hole or actively negotiate with primary users, i.e the existing licensed users, to access the spectrum. There have been plenty of researches on CR-related topic, which could be classified into three fundamental tasks [3]: 1. Radio-scene analysis, which includes estimation of interfer-ence temperature of the radio environment and detection of spectrum holes. 2. Channel state estimation and predictive modeling, which encompasses estimation of channel-state information and prediction of channel capacity for use by the transmitter. 3. Transmit power control and dynamic spectrum management.
Our work is focus on detecting the activity level of primary users under fading envi-ronments, aiming at avoiding interference to licensed users for promoting the coexistence with underlying primary system, which we adopt an alternative view in sequential sense contrary to conventional block-based detection to tackle with.