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.

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Design and analysis of cooperative communication schemes based upon modeling and simulation exist in large quantities in the research literature. Despite this fact, there have been relatively few efforts directed toward implementing and experimentally evaluating such schemes. Cooperative protocols have many components that make them challenging to implement in real-world radio architectures, and their expected gains are highly dependent on the network topology and RF environment in which they operate. As such, experimental work will be crucial in the transition of such schemes from conceptual proposals to next-generation wireless standards. This paper motivates such practical work, surveys existing efforts in the area, and offers future direction for architectural and experimental design.

Abstract-Block Markov superposition encoding has been used on a number of channels to enable transmitter cooperation, including the decode-and-forward (DF) relaying scheme on the full-duplex relay channel. We analyze the error performance of DF with regular encoding and sliding window decoding as the window size of the decoder is allowed to grow. Specifically, we use Gallager's random coding exponent to analyze the behavior of DF in the finite block length regime where the error probability cannot be made arbitrarily small for a fixed rate and block length. Although using a larger decoding window may not result in a better achievable rate in the infinite block length regime, doing so for finite block lengths enables a higher rate of transmission for a given error probability. In particular, these rate enhancements can lead to a larger range of operating scenarios in which relaying can outperform direct transmission.

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