Sequence Detection Algorithms for PHY-Layer Sensing in Dynamic Spectrum Access Networks

Abstract

Abstract-Spectrum sensing is a critical function for enabling dynamic spectrum access (DSA) in wireless networks that utilize cognitive radio (CR). In DSA networks, unlicensed secondary users can gain access to a licensed spectrum band as long as they do not cause harmful interfere to primary users. Spectrum sensing is subject to errors in the form of false alarms and missed detections. False alarms cause spectrum under-use by secondary users, and missed detections cause interference to primary users. Although existing research has demonstrated the utility of a Markov chain for modeling the spectrum access pattern of primary users over time, little effort has been directed toward spectrum sensing based upon such models. In this paper, we develop general sequence detection algorithms for Markov sources in noise for spectrum sensing in DSA networks. We assign different Bayesian cost factors for missed detections and false alarms, and we show that a suitably modified forwardbackward sequence detection algorithm is optimal in minimizing the detection risk. Two advanced sequence detection algorithms, the complete forward algorithm and the complete forward partial backward algorithm are introduced and their performances are compared as well. Along the way, we observe new fundamental limitations on sensing performance that we term the risk floor and the window length limitation of energy detection and coherent detection that arise from mismatch of their observation window with the PU's spectrum access pattern.

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

“…Third, the Markov chain, the linear regression and the ARMA models can be used in the network layer for resource management. All of them will improve the system performance, as shown in [2] - [10].…”

“…The instantaneous accuracy depends on the specific realization of the spectrum occupancy as a random variable or random process and varies from realization to realization, but its performance can be improved on average by using its statistical behaviours, as in [2] - [10]. This is also the purpose of many other statistical detection and estimation applications that aim for average performance improvement rather than instantaneous performance improve- ment, such as average bit error rate improvement in fading channels.…”

“…To name a few, in [2], the authors used the Markov chain model of the spectrum occupancy in the Bayesian cost factors of missed detection and false alarm to enhance the accuracy of spectrum sensing. The proposed weighted sequence detection algorithm is optimal in minimizing the overall detection error.…”

Low latency relaying schemes for next-generation cellular networks

“…In the case of convolutional encoding at the source, this can be accomplished by modifying the BCJR algorithm as in [12], [13].…”

Secondary access policies with imperfect sensing in dynamic spectrum access networks

“…(21) specifies the stationary probabilities that the channel is in the state ω, and Eq. (22) relies on a forward propagation similar in structure to the algorithms we developed in [12]. Let γ t (ω) be the a posteriori probability that the hidden state at time t is ω for the given observation sequence o t .…”