There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.

In spectrum sensing, if the primary user (PU) signal and the channel noise both follow Gaussian distribution and neither of their probability distribution functions (PDFs) are known, the traditional approaches based on entropy or Likelihood Ratio Test (LRT) etc., become infeasible. To address this problem, we propose a spectrum sensing method that exploits the similarity of PDFs of two time-adjacent detected data sets with cross entropy, while accounting for achieving the detection performance of LRT which is Neyman-Pearson optimal in detecting the primary user. We show that the detection performance of the proposed method asymptotically approximates that of LRT in detecting the PU. The simulation results confirm our analysis.

Solution-based organic field-effect transistors (OFETs) with low parasitic capacitance have been fabricated using a self-aligned method. The self-aligned processes using a cross-linking polymer gate insulator allow fabricating electrically stable polymer OFETs with small overlap area between the source-drain electrodes and the gate electrode, whose frequency characteristics have been investigated by impedance spectroscopy (IS). The IS of polymer OFETs with self-aligned electrodes reveals frequency-dependent channel formation process and the frequency response in FET structure.

Dynamic spectrum allocation (DSA) based on secondary spectrum market is considered a promising technology to improve spectrum utilization efficiency and to relieve the wireless spectrum shortage problem. We propose a dynamic spectrum allocation algorithm named market equilibrium and game (MEG), and construct a complete secondary spectrum market. The market based on the MEG algorithm consists of two submarkets: multiple primary services providers (PSPs) and a dynamic spectrum allocation server (DSAS) form the high submarket, while the low submarket is composed of the DSAS and a number of secondary users. In the low submarket, the MEG algorithm provides a game type selection strategy. By this strategy, the DSAS can win more payoffs with lower unit spectrum price, which encourages secondary users to use more spectrum. A secondary user can also choose its preferable game type between dynamic game and Nash bargaining flexibly. On the other hand, a bargaining procedure in the high submarket is designed in the MEG algorithm to ensure that market equilibrium is quickly reached. A performance analysis shows that the strategy of game type selection is fair and feasible for both the DSAS and the secondary users. Moreover, the bargaining procedure is better than the existing algorithm which adjusts price step by step in the high submarket. Simulation results also demonstrate that the market fluctuation in the low submarket is passed to the high submarket by way of the DSAS. The MEG algorithm can effectively satisfy the highly-fluctuating demands from the secondary users. In addition, the MEG algorithm can improve the payoffs of all players and increase spectrum utilization efficiency.

We propose a simple approximate model for unslotted opportunistic spectrum access networks under nonsaturation conditions. The main simplification we introduce is that all secondary users, except a tagged one, in nonsaturated setting can be approximated by saturated ones with a scaled version of backoff interval. We analyze the approximate model and verify the model using simulations.

In information-spectrum methods proposed by Han and Verdu, quantities defined by using the limit superior (or inferior) in probability play crucial roles in many problems in information theory. In this paper, we introduce two nonconventional quantities defined in probabilistic ways. After clarifying basic properties of these quantities, we show that the two quantities have operational meaning in the ε-coding problem of a general source in the ordinary and optimistic senses. The two quantities can be used not only for obtaining variations of the strong converse theorem but also establishing upper and lower bounds on the width of the entropy-spectrum. We also show that the two quantities are expressed in terms of the smooth Renyi entropy of order zero.

Concomitantly with the progress of wireless communications, cognitive radio has attracted attention as a solution for depleted frequency bands. Cognitive radio is suitable for wireless sensor networks because it reduces collisions and thereby achieves energy-efficient communication. To make cognitive radio practical, we propose a low-power multi-resolution spectrum sensing (MRSS) architecture that has flexibility in sensing frequency bands. The conventional MRSS scheme consumes much power and can be adapted only slightly to process scaling because it comprises analog circuits. In contrast, the proposed architecture carries out signal processing in a digital domain and can detect occupied frequency bands at multiple resolutions and with low power. Our digital MRSS module can be implemented in 180-nm and 65-nm CMOS processes using Verilog-HDL. We confirmed that the processes respectively dissipate 9.97 mW and 3.45 mW.

The multiple-access channel (MAC) becomes very popular in various communication systems, because multi-terminal communication systems have been widely used in practical systems, e.g., mobile phones and P2P, etc. For some MACs, it is known that feedback can enlarge the capacity region, where the capacity region is the set of rate pairs such that the error probability can be made arbitrarily small for sufficiently large block length. The capacity region for general MACs, which are not required to satisfy ergodicity and stationarity with perfect feedback was first shown by Tatikonda and Mitter without the proof, where perfect feedback means that the channel output is perfectly fed back to senders. In this paper, we generalize Tatikonda and Mitter's result to the case of deterministic feedback, where the values of deterministic functions of past channel outputs is fed back to senders. We show that the capacity region for general MACs with deterministic feedback can be represented by the information-spectrum formula introduced by Han and Verdu, and directed information introduced by Massey. We also investigate the compound MAC problem, the ε-coding problem, the strong converse property and the cost constraint problem for general MACs with deterministic feedback.

In non-cooperative scenarios, the estimation of direct sequence spread spectrum (DS-SS) signals has to be done in a blind manner. In this letter, we consider the spreading sequence estimation problem for DS-SS signals. First, the maximum likelihood estimate (MLE) of spreading sequence is derived, then a semidefinite relaxation (SDR) approach is proposed to cope with the exponential complexity of performing MLE. Simulation results demonstrate that the proposed approach provides significant performance improvements compared to existing methods, especially in the case of low numbers of data samples and low signal-to-noise ratio (SNR) situations.

In this paper, we present a cognitive relay network with two primary transceivers that communicate via several distributed relay terminals. Spectrum sensing is deployed at the relays to sense the absence/presence of the primary transceivers based on energy detection. The primary network utilizes a two-step two-way amplify-and-forward (AF) scheme by using the cognitive radio (CR) terminals as its relay nodes when the primary network is not in operation, in contrast, the CRs communicate with their own base station (BS). In the first relaying step, the primary transceivers send their signal to the CRs/relays. Distributed beamforming is then performed in the second relaying step. Our aim is to set the beamforming weights so as to minimize the total power dissipated in the relay network while satisfying a target signal-to-noise ratio (SNR) at the primary transceivers and at the cognitive BS. This is achieved by solving an optimization problem that we formulate as a nonconvex quadratically constrained quadratic program (QCQP). This problem is solved efficiently by semidefinite relaxation (SDR) and Lagrangian duality. Simulation results are provided to demonstrate the superiority of our proposed technique, compared to classical beamforming techniques, in terms of power reduction.

In this paper, we present a distributed and interactive admission and power control protocol for spectrum underlay environments. The protocol enables distributed primary users (PUs) to estimate and adjust the level of tolerable interference as their transmitting powers evolve to a given signal-to-interference-plus-noise ratio (SINR) target. The protocol also guides the powers of distributed secondary users (SUs) to achieve their own targets while restricting the transmitting powers from SUs so as not to interfere with the PUs. This restriction of interference from SUs to PUs is an essential part of cognitive radio networks (CRNs) and is facilitated by sending a warning tone from PUs to SUs in the proposed protocol. The SUs that have frequently received the warning tones turn off their transmitters and so autonomously drop from the system. This paper proves that, under the proposed interactive protocol, every PU finally achieves its target if it is originally feasible without SUs and the transmit powers of remaining SUs converge to a fixed point. The proposed method protects PUs perfectly in the sense that all the PUs reach their targets after power control. Numerical investigation shows how safely PUs are protected and how well SUs are admitted as a function of protocol parameters, the frequency of warning tones, the number of SUs to be admitted and the number of active PUs.

In this paper, we propose a spectral difference approach for noise power estimation in speech enhancement. The noise power estimate is given by recursively averaging past spectral power values using a smoothing parameter based on the current observation. The smoothing parameter in time and frequency is adjusted by the spectral difference between consecutive frames that can efficiently characterize noise variation. Specifically, we propose an effective technique based on a sigmoid-type function in order to adaptively determine the smoothing parameter based on the spectral difference. Compared to a conventional method, the proposed noise estimate is computationally efficient and able to effectively follow noise changes under various noise conditions.

Dynamic spectrum access (DSA) has drawn immediate attention recently since it can opportunistically exploit any spectrum holes and thus improve bandwidth utilization. From the perspective of medium access control (MAC) design, the QoS requirement of SU is one of the design issues in DSA network. In this paper, we propose a new admission control scheme referred to as log-based dynamic spectrum access admission control (DSAC) aiming at (1) protection of the primary users and (2) QoS prioritization for the existing secondary users. The DSAC algorithm protects the PU by limiting SUs' access using PU's arrival log or statistics. Furthermore, the DSAC reserves a channel for previously admitted SU to reduce frequent service disruption of the SU. Reservation of channels is carried out without assuming any specific arrival process, and thus the DSAC would be practical for general user arrival patterns unlike the existing admission control techniques. Performance evaluation has shown that the proposed DSAC outperforms existing admission control schemes with respect to the PU blocking rate, SU communication stability, and SU aggregate throughput by about 13%, 26%, and 20%, respectively.

A hybrid overlay/underlay spectrum sharing method for cognitive radio networks based on user classification and convex optimization is proposed. Interference radii are configured for the primary receiver and each cognitive receiver. Cognitive users are divided into four groups and allocated different spectrum sharing patterns according to their distance from the primary transmitter and receiver. An optimal power allocation scheme that achieves the maximum sum rate of cognitive radio system on the premise of satisfying the interference constraint of primary receiver is acquired through the convex optimization method. Performance analysis and simulation results show that, compared with existing methods, our method leads to improved performance of achievable sum rate of cognitive users while guarantees the transmission of primary users.

Cognitive radio (CR) is a key solution for the problem of inefficient usage of spectral resources. Spectrum sensing in each CR aims at detecting whether a preassigned spectrum band is occupied by a primary user or not. Conventional techniques do not allow the CR to communicate with its own base station during the spectrum sensing process. So, only a part of the frame can be used for cognitive data transmission. In this paper, we introduce a new spectrum sensing framework that combines a blind source separation technique with conventional spectrum sensing techniques. In this way, the cognitive transmitter can continue to transmit during spectrum sensing, if it was in operation in the previous frame. Moreover, the accuracy is improved since the decision made by the spectrum unit in each frame depends on the decision made in the previous frame. We use Markov chain tools to model the behavior of our spectrum sensing proposal and to derive the parameters that characterize its performance. Numerical results are provided to confirm the superiority of the proposed technique compared to conventional spectrum sensing techniques.

A spectrum sensing scheme for cognitive radio that includes coarse and fine sensing stages based on cyclostationarity is proposed in this paper. The cyclostationary feature detection (CFD) based on a single cyclic frequency (SCF) is used in the coarse sensing stage and that based on multiple cyclic frequencies (MCF) is employed in the fine sensing stage. Whether the fine sensing stage is performed or not is decided by comparing the statistic constructed in the coarse sensing stage with two thresholds. Theoretical analyses and simulation results show that the proposed sensing scheme has superior sensing performance and needs shorter sensing time.

In this letter, we focus on the spectrum sharing cognitive radio system, wherein a single-input multi-output cognitive fading channel is considered. Subject to the joint average interference constraint and peak interference constraint at the primary receiver, the outage capacity of the cognitive channel involving joint beamforming and power control is analyzed. We derive the optimal beamforming and power control strategy and deduce the closed-form expression for the outage capacity under Rayleigh fading model, the functional regions of two kinds of interference constraints are discussed as well. Furthermore, considering zero-outage transmission, we investigate the delay-limited capacity and introduce a new concept called the zero-outage average interference wall. Extensive simulations corroborate our theoretical results.

In this paper, a preemptive priority queueing model is developed to derive the system dwelling time of secondary calls in a cognitive radio system in which a primary call's reoccupation of the channel is modeled as a preemptive event that forces a secondary call to attempt a spectrum handover. The suspension of secondary call service which may happen when the immediate spectrum handover fails, is included in our computation of the system dwelling time. The results are helpful in evaluating cognitive radio systems in terms of service delay and in determining system design parameters such as required buffer size and system capacity.

Digital RF solutions have been shown to be advantageous in various design aspects, such as accurate modeling, design reuse, and scaling when migrating to the next CMOS process node. Consequently, the majority of new low-cost and feature cell phones are now based on this approach. However, another equally important aspect of this approach to wireless transceiver SoC design, which is instrumental in allowing fast and low-cost productization, is in creating the inherent capability to assess performance and allow for low-cost built-in calibration and compensation, as well as characterization and final-testing. These internal capabilities can often rely solely on the SoCs existing processing resources, representing a zero cost adder, requiring only the development of the appropriate algorithms. This paper presents various examples of built-in measurements that have been demonstrated in wireless transceivers offered by Texas Instruments in recent years, based on the digital-RF processor (DRPTM) technology, and highlights the importance of the various types presented; built-in self-calibration and compensation, built-in self-characterization, and built-in self-testing (BiST). The accompanying statistical approach to the design and productization of such products is also discussed, and fundamental terms related with these, such as 'soft specifications', are defined.

In this letter, we propose a Partially Observable Markov Decision Process (POMDP) based Distributed Adaptive Opportunistic Spectrum Access (DA-OSA) Strategy for Cognitive Ad Hoc Networks (CAHNs). In each slot, the source and destination choose a set of channels to sense and then decide the transmission channels based on the sensing results. In order to maximize the throughput for each link, we use the theories of sequential decision and optimal stopping to determine the optimal sensing channel set. Moreover, we also establish the myopic policy and exploit the monotonicity of the reward function that we use, which can be used to reduce the complexity of the sequential decision.