Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access

Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access (DSA). Too high sampling rate is the main problem. Compressive sensing (CS) can reconstruct sparse signal with much fewer randomized samples than Nyquist sampling with high probability. Since survey shows that the monitored signal is sparse in frequency domain, CS can deal with the sampling burden. Random samples can be obtained by the analog-to-information converter. Signal recovery can be formulated as an L0 norm minimization and a linear measurement fitting constraint. In DSA, the static spectrum allocation of primary radios means the bounds between different types of primary radios are known in advance. To incorporate this a priori information, we divide the whole spectrum into subsections according to the spectrum allocation policy. In the new optimization model, the minimization of the L2 norm of each subsection is used to encourage the cluster distribution locally, while the L0 norm of the L2 norms is minimized to give sparse distribution globally. Because the L0/L2 optimization is not convex, an iteratively re-weighted L1/L2 optimization is proposed to approximate it. Simulations demonstrate the proposed method outperforms others in accuracy, denoising ability, etc.Comment: 23 pages, 6 figures, 4 table. arXiv admin note: substantial text overlap with arXiv:1005.180

Effective Capacity in Cognitive Radio Broadcast Channels

In this paper, we investigate effective capacity by modeling a cognitive radio broadcast channel with one secondary transmitter (ST) and two secondary receivers (SRs) under quality-of-service constraints and interference power limitations. We initially describe three different cooperative channel sensing strategies with different hard-decision combining algorithms at the ST, namely OR, Majority, and AND rules. Since the channel sensing occurs with possible errors, we consider a combined interference power constraint by which the transmission power of the secondary users (SUs) is bounded when the channel is sensed as both busy and idle. Furthermore, regarding the channel sensing decision and its correctness, there exist possibly four different transmission scenarios. We provide the instantaneous ergodic capacities of the channel between the ST and each SR in all of these scenarios. Granting that transmission outage arises when the instantaneous transmission rate is greater than the instantaneous ergodic capacity, we establish two different transmission rate policies for the SUs when the channel is sensed as idle. One of these policies features a greedy approach disregarding a possible transmission outage, and the other favors a precautious manner to prevent this outage. Subsequently, we determine the effective capacity region of this channel model, and we attain the power allocation policies that maximize this region. Finally, we present the numerical results. We first show the superiority of Majority rule when the channel sensing results are good. Then, we illustrate that a greedy transmission rate approach is more beneficial for the SUs under strict interference power constraints, whereas sending with lower rates will be more advantageous under loose interference constraints.Comment: Submitted and Accepted to IEEE Globecom 201

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Smart Sensing and Performance Analysis for Cognitive Radio Networks

Static spectrum access policy has resulted in spectrum scarcity as well as low spectrum utility in today\u27s wireless communications. To utilize the limited spectrum more efficiently, cognitive radio networks have been considered a promising paradigm for future network. Due to the unique features of cognitive radio technology, cognitive radio networks not only raise new challenges, but also bring several fundamental problems back to the focus of researchers. So far, a number of problems in cognitive radio networks have remained unsolved over the past decade. The work presented in this dissertation attempts to fill some of the gaps in the research area of cognitive radio networks. It focuses primarily on spectrum sensing and performance analysis in two architectures: a single cognitive radio network and multiple co-existing cognitive radio networks. Firstly, a single cognitive radio network with one primary user is considered. A weighted cooperative spectrum sensing framework is designed, to increase the spectrum sensing accuracy. After studying the architecture of a single cognitive radio network, attention is shifted to co-existing multiple cognitive radio networks. The weakness of the conventional two-state sensing model is pointed out in this architecture. To solve the problem, a smart sensing model which consists of three states is designed. Accordingly, a method for a two-stage detection procedure is developed to accurately detect each state of the three. In the first stage, energy detection is employed to identify whether a channel is idle or occupied. If the channel is occupied, received signal is further analyzed at the second stage to determine whether the signal originates from a primary user or an secondary user. For the second stage, a statistical model is developed, which is used for distance estimation. The false alarm and miss detection probabilities for the spectrum sensing technology are theoretically analyzed. Then, how to use smart sensing, coupled with a designed media access control protocol, to achieve fairness among multiple CRNs is thoroughly investigated. The media access control protocol fully takes the PU activity into account. Afterwards, the significant performance metrics including throughput and fairness are carefully studied. In terms of fairness, the fairness dynamics from a micro-level to macro-level is evaluated among secondary users from multiple cognitive radio networks. The fundamental distinctions between the two-state model and the three-state sensing model are also addressed. Lastly, the delay performance of a cognitive radio network supporting heterogeneous traffic is examined. Various delay requirements over the packets from secondary users are fully considered. Specifically, the packets from secondary users are classified into either delay-sensitive packets or delay-insensitive packets. Moreover, a novel relative priority strategy is designed between these two types of traffic by proposing a transmission window strategy. The delay performance of both a single-primary user scenario and a multiple-primary user scenario is thoroughly investigated by employing queueing theory