An important and widely considered signal identification technique for cognitive radios is cyclostationarity-based feature detection because this method does not require time and frequency synchronization and prior information except for information concerning cyclic autocorrelation features of target signals. This paper presents the development and experimental evaluation of cyclostationarity-based signal identification equipment. A spatial channel emulator is used in conjunction with the equipment that provides an environment to evaluate realistic spectrum sharing scenarios. The results reveal the effectiveness of the cyclostationarity-based signal identification methodology in realistic spectrum sharing scenarios, especially in terms of the capability to identify weak signals.

Cognitive radio is an emerging technology to further improve the efficiency of spectrum use. Due to the nature of the technology, it has many facets, including its enabling technologies, its implementation issues and its regulatory implications. In ITU-R (International Telecommunications Union – Radiocommunication sector), cognitive radio systems are currently being studied so that ITU-R can have a clear picture on this new technology and its potential regulatory implications, from a viewpoint of global spectrum management. This paper introduces the recent results of the ITU-R studies on cognitive radio on both regulatory and technical aspects. This paper represents a personal opinion of the author, but not an official view of the ITU-R.

This letter proposes a novel connected dominanting set based decentralized cooperative spectrum sensing algorithm for cognitive radio networks. It is analytically shown that the proposed algorithm distributively converges to the average consensus as that of traditional distributed consensus algorithm, while reducing both the convergence time and message complexity significantly.

We propose a new cognitive radio network architecture using the IP multimedia subsystem (IMS) functionality. We implement the cognitive radio network entities standardized in IEEE 1900.4 on the IMS that exchanges RAN and terminal context information between the networks and the terminals to make optimum and immediate reconfiguration decisions. In our proposed architecture, RAN context information is obtained from cellular networks which are directly connected to the IMS. The presence management functions of the IMS are applied to exchange those information in a “push” manner, which enables immediate notification of changes in wireless environment. We evaluate the performance of the proposed context information exchange method, by comparing with the cases that adequate and immediate RAN context information is not available. The evaluation results show that the proposed framework gives 10–30% superior performance than the conventional cognitive radio networks.

This paper introduces a unified method of spectrum sensing for all existing analog television (TV) signals including NTSC, PAL and SECAM. We propose a correlation based method (CBM) with a single reference signal for sensing any analog TV signals. In addition we also propose an improved energy detection method. The CBM approach has been implemented in a hardware prototype specially designed for participating in Singapore TV white space (WS) test trial conducted by Infocomm Development Authority (IDA) of the Singapore government. Analytical and simulation results of the CBM method will be presented in the paper, as well as hardware testing results for sensing various analog TV signals. Both AWGN and fading channels will be considered. It is shown that the theoretical results closely match with those from simulations. Sensing performance of the hardware prototype will also be presented in fading environment by using a fading simulator. We present performance of the proposed techniques in terms of probability of false alarm, probability of detection, sensing time etc. We also present a comparative study of the various techniques.

It is important to optimize aggregation schemes for heterogeneous wireless networks for maximizing communication throughput utilizing any available radio access networks. In the heterogeneous networks, differences of the quality of service (QoS), such as throughput, delay and packet loss rate, of the networks makes difficult to maximize the aggregation throughput. In this paper, we firstly analyze influences of such differences in QoS to the aggregation throughput, and show that it is possible to improve the throughput by adjusting the parameters of an aggregation system. Since manual parameter optimization is difficult and takes much time, we propose an autonomous parameter tuning scheme using a machine learning algorithm for the heterogeneous wireless network aggregation. We implement the proposed scheme on a heterogeneous cognitive radio network system. The results on our experimental network with network emulators show that the proposed scheme can improve the aggregation throughput better than the conventional schemes. We also evaluate the performance using public wireless network services, such as HSDPA, WiMAX and W-CDMA, and verify that the proposed scheme can improve the aggregation throughput by iterating the learning cycle even for the public wireless networks. Our experimental results show that the proposed scheme achieves twice better aggregation throughput than the conventional schemes.

One important issue in cognitive transmission is for multiple secondary users to dynamically acquire spare spectrum from the single primary user. The existing spectrum sharing scheme adopts a deterministic Cournot game to formulate this problem, of which the solution is the Nash equilibrium. This formulation is based on two implicit assumptions. First, each secondary user is willing to fully exchange transmission parameters with all others and hence knows their complete information. Second, the unused spectrum of the primary user for spectrum sharing is always larger than the total frequency demand of all secondary users at the Nash equilibrium. However, both assumptions may not be true in general. To remedy this, the present paper considers a more realistic assumption of incomplete information, i.e., each secondary user may choose to conceal their private information for achieving higher transmission benefit. Following this assumption and given that the unused bandwidth of the primary user is large enough, we adopt a probabilistic Cournot game to formulate an opportunistic spectrum sharing scheme for maximizing the total benefit of all secondary users. Bayesian equilibrium is considered as the solution of this game. Moreover, we prove that a secondary user can improve their expected benefit by actively hiding its transmission parameters and increasing their variance. On the other hand, when the unused spectrum of the primary user is smaller than the maximal total frequency demand of all secondary users at the Bayesian equilibrium, we formulate a constrained optimization problem for the primary user to maximize its profit in spectrum sharing and revise the proposed spectrum sharing scheme to solve this problem heuristically. This provides a unified approach to overcome the aforementioned two limitations of the existing spectrum sharing scheme.

of late, many researchers have been interested in sparse representation of signals and its applications such as Compressive Sensing in Cognitive Radio (CR) networks as a way of overcoming the issue of limited bandwidth. Compressive sensing based wideband spectrum sensing is a novel approach in cognitive radio systems. Also in these systems, using spatial-frequency opportunistic reuse is emerged interestingly by constructing and deploying spatial-frequency Power Spectral Density (PSD) maps. Since the CR sensors are distributed in the region of support, the sensed PSD by each sensor should be transmitted to a master node (base-station) in order to construct the PSD maps in space and frequency domains. When the number of sensors is large, this data transmission which is required for construction of PSD map can be challenging. In this paper, in order to transmit the CR sensors' data to the master node, the compressive sensing based scheme is used. Therefore, the measurements are sampled in a lower sampling rate than of the Nyquist rate. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 30% of full data transmission. Also, simulation results show the robustness of the proposed method against the channel variations in comparison with classical methods. Different solution schemes such as Basis Pursuit, Lasso, Lars and Orthogonal Matching Pursuit are used and the quality performance of them is evaluated by several simulation results over a Rician channel with respect to several different compression and Signal to Noise Ratios. It is also illustrated that the performance of Basis Pursuit and Lasso methods outperform the other compression methods particularly in higher compression rates.

This letter investigates the problem of distributed channel selection in cognitive radio ad hoc networks (CRAHNs) with heterogeneous spectrum opportunities. Firstly, we formulate this problem as a local congestion game, which is proved to be an exact potential game. Then, we propose a spatial best response dynamic (SBRD) to rapidly achieve Nash equilibrium via local information exchange. Moreover, the potential function of the game reflects the network collision level and can be used to achieve higher throughput.

In this letter, we consider a cognitive radio based multihop network under the spectrum sharing underlay paradigm. By taking into account the interference constraints, we present an exact closed-form expression for outage probability, which is valid for the whole signal-to-noise ratio regime. In addition, some numerical examples of interest that study the effect of the number of hops and/or the interferer threshold on primary users are illustrated and discussed. Numerical results show that multihop systems still offer a considerable gain as compared to direct transmission under the same limit of interference.

Spectrum sensing, a key technical challenge in cognitive radios (CR) technology, is a technique that enables the spectrum of licensed systems to be accessed without causing undue interference. It is well known that cyclostationarity detectors have great advantages over energy detectors in terms of the robustness to noise uncertainty that significantly degrades the performance as well as the capability to distinguish the signal of interest from the other interferences and noise. The generalized likelihood ratio test (GLRT) is a recognized sensing technique that utilizes the inherent cyclostationarity of the signal and has been intensively studied. However, no comprehensive evaluation on its performance enhancement has been published to date. Moreover high computational complexity is still a significant problem for its realization. This paper proposes a maximum ratio combining multi-cyclic detector which uses multiple cyclic frequencies for performance enhancement with reduced computational complexity. An orthogonal frequency-division multiplexing (OFDM) signal based on the ISDB-T (integrated services digital broadcasting terrestrial), a Japanese digital television broadcasting standard, was used in the evaluation assuming this as a primary system in WRAN (wireless regional area network) applications like IEEE 802.22.

In this letter, we propose an adaptive sensing/transmission scheduling policy in which the secondary user senses the spectrum when its channel condition is poor for transmission. The adaptive sensing/transmission scheduling is modeled as a Markov process and a near-optimal algorithm is proposed to determine the sensing/transmission policy. Simulation results verify our analysis and demonstrate the superiority of the proposed algorithm.

This letter considers a multiple-channel cognitive radio network (CRN) which can simultaneously sense multiple narrowband channels at a time. Taking the maximization of the CRN's overall throughput as the design objective, the optimization problem of jointly designing sensing time, sensing thresholds and transmission power allocation is formulated under the total power constraint of the CRN and the average interference constraint of the primary network. An iterative algorithm is proposed to obtain the locally optimal values for these parameters. Finally, numerical results show that significant overall throughput gain is achieved through the joint design.

To opportunistically use the licensed band, spectrum sensing has a vital role as the core component in cognitive radio systems. However, the accurate detection of the primary signal is always accompanied by significant overhead, reducing the secondary throughput. In this letter, we suggest remedying this problem by adopting multiple-stage spectrum sensing (MSS) technique. Furthermore, we investigate how our proposed MSS can be incorporated into the collaborative spectrum sensing. Our results are encouraging in that the proposed MSS with collaboration significantly reduces the sensing time compared to the conventional sensing scheme.

In this paper, a simple type of printed dipole is proposed for Multi-Input Multi-Output (MIMO) applications in cognitive radio. The antenna is composed of a transmission line and a dipole. Some examinations of key factors and optimized parameters of the antenna are presented. The measured results illustrate that the proposed antenna offers a bandwidth of over 50% for Voltage Standing Wave Ratio (VSWR) less than 2, extending from 2.4 GHz to 4.0 GHz. The antenna peak gain in E-plane and radiation patterns at different frequencies are also explored. In addition, based on the proposed antenna, we introduce two simple broadband arrays for MIMO applications in cognitive radio. One has two ports and the other has four ports. Measurement results indicate that the arrays also work in a broad bandwidth. Mutual couplings between ports in each array are kept under -10 dB at the low frequencies and under -20 dB at the high frequencies of bandwidth of the arrays. Furthermore, we utilized the antenna arrays for some MIMO experiments to estimate the channel capacity in a wide frequency range.

This paper presents the efficiency of a sensing database and caching (SDB) for cognitive radio systems. The proposed SDB stores regulatory information from regulatory databases, and contains sensing information by distributed sensing schemes. Preliminary information processing for instance indexing, sorting, or applying some models or algorithms, etc. can be performed for the stored data. Available information and the results of the information processing are provided to cognitive radios in order to determine available spectrum and to facilitate dynamic spectrum access at lower sensing cost but higher sensing quality. The SDB is implemented in local networks, therefore information exchange between SDB and the cognitive radios can be realized at low latency and the amount of signaling traffic to global network can be reduced. This paper analyzes the effect of SDB and the performance evaluation was done in a certain condition. As a result, by deploying SDB a system can achieve up to 20% of reduction of sensing activities and maximum 1.3 times higher sensing quality.

Cognitive radio (CR) in spectrum sharing mode allows secondary user (SU) to share the same spectrum simultaneously with primary user (PU), as long as the former guarantees no harmful interference is caused to the latter. This letter proposes a new type of constraint to protect the PU systems that are carrying delay-sensitive applications, namely the PU effective capacity loss constraint, which sets an upper bound on the maximum effective capacity loss of the PU due to the SU transmission. In addition, the PU effective capacity loss constraint is approximately transformed to the interference temperature (power) constraint, to make it easier to be implemented. As an example, we obtain a closed form expression of the SU effective capacity under the approximated peak interference power constraint and the results of simulations validate the proposed PU protection criterion.

We study joint rate control and resource allocation with a packet collision constraint that maximizes the total utility of secondary users in cognitive radio networks. We formulate and decouple the original optimization problem into separable subproblems and then develop an algorithm that converges to optimal rate control and resource allocation. The proposed algorithm can operate on different time-scales to reduce the amortized time complexity.

Cognitive radio technology, which allows secondary user (SU) to utilize the spectrum holes left by primary user (PU), was proposed to solve spectrum under-utilization problem. However, due to sensing error, SU's transmission will bring negative effects to PU's communication. Recently, cooperative relay technology was introduced to solve this problem. In this paper, a cooperative framework, which allows SU to act as a relay for primary link when needed, is considered and then a cognitive relay scheme is proposed. In order to maximize SU's throughput while keeping the system stable, we study and obtain SU's optimal strategy (i.e., relaying strategy and power allocation) by a constrained optimization problem. Since energy consumption is also an important problem for cognitive radio networks, we also investigate SU's optimal strategy to maximize SU's energy efficiency while keeping the system stable. The numerical results show that the cognitive relay scheme can achieve higher throughput and energy efficiency than reference schemes.

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.