Spectrum Opportunity Detection Research Articles

Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks

Cognitive radio networks have emerged as a promising solution to the growing problem of spectrum scarcity and inefficient spectrum utilization. By enabling secondary users to opportunistically access underutilized frequency bands while avoiding harmful interference to primary users, cognitive radio networks offer the potential for more efficient and dynamic spectrum allocation. In this paper, we propose an intelligent approach to dynamic spectrum access using fuzzy logic in cognitive radio networks. The key challenge in cognitive radio networks lies in spectrum sensing, decision-making, and channel selection, where uncertainties and variations in the radio frequency spectrum are prevalent. To address these challenges, we employ fuzzy logic as a powerful tool to handle the inherent imprecision and ambiguity in the decision-making process. Fuzzy-based spectrum sensing algorithms allow for robust detection of spectrum opportunities by considering the “fuzziness” in the received signal strength and noise conditions.

Spectrum Opportunity Detection in Cellular Networks

The signal propagation over wireless channels cannot be predicted perfectly due to numerous factors such as fading, channel interference and obstacles. An interference footprint is required to be estimated accurately for evaluation of the spatial spectrum opportunity. It is difficult to determine the spatial spectrum opportunities available in uplink bands of cellular networks due to different location of primary users at different times. In this research work, spatial spectrum opportunity in uplink bands of cellular network is determined using an efficient computational geometry tool for realistic scenario. Our results shows that the performance of umbrella footprints based approach is better than that of conventional circular footprints based approach in terms of false alarm and missed detection probabilities.

UAV-Based 3D Spectrum Sensing in Spectrum-Heterogeneous Networks

In cognitive radio networks, collaborative spectrum sensing has been recognized as a key technology to enable secondary users (SUs) to detect spectrum holes and opportunistically access primary licensed spectrum band without harmful interference. Most of the existing studies focus on 1D or 2D spectrum opportunity detection and explicitly or implicitly assume that all SUs share the same spectrum opportunity. In this paper, we first investigate the issue of joint spatial-temporal spectrum sensing in 3D spectrum-heterogeneous space by leveraging the location flexibility of flying unmanned aerial vehicle (UAV) spectrum sensors. We divide the sensing space into three layers: black layer, grey layer, and white layer, which represent different spatial spectrum access opportunities. Then, we formulate the 3D spatial-temporal opportunity sensing model and derive the spatial-temporal false alarm and detection probabilities at both single UAV level and whole network level. Afterwards, we design a temporal fusion window and a spatial fusion sphere to address the composite spatial-temporal data fusion, called 3D spatial-temporal sensing (3DSTS). A comparison among 3DSTS, the benchmark 3D non-cooperative sensing (3DNCS) and 3D cooperative sensing (3DCS) shows the inefficiency of the traditional sensing schemes in the 3D spectrum-heterogeneous networks. Furthermore, we develop three improved versions of 3DSTS, which show much better detection performance. To maximize the utilization of spectrum resource and minimize the interference to the PU, a sensing-based power control scheme is also proposed. Finally, numerical simulations corresponding to the theoretical analysis are demonstrated.

Co-SpOT: Cooperative Spectrum Opportunity Detection Using Bayesian Clustering in Spectrum-Heterogeneous Cognitive Radio Networks

A Bayesian data-mining approach is introduced for dynamic spectrum sensing in spectrum-heterogeneous cognitive radio networks. The goal is to find the spectrum opportunities in temporal, spectral, and spatial domains in the presence of unreliable sensing data and unavailable location information. In spectrum-heterogeneous networks, the availability of spectrum varies over the space and different sensors experience different spectrum opportunities. Thus, the measurements from sensors cannot be simply aggregated to detect the spectrum opportunities. Moreover, unreliable data will negatively impact the decision-making process. The task of inferring the spectrum status becomes even more challenging when the sensors are not equipped with location-finding technologies. In this paper, we propose a probabilistic model to cluster the sensors solely based on their observations, not requiring any prior knowledge of the network topology, location of the sensors, or the number of clusters. After receiving the sensing data, the base station updates the probability distributions of cluster membership, channel availability, and device reliability. All the update rules are derived mathematically by the variational inference. Then, the distributions are employed to find spectrum opportunities via multi-label graph cuts method. Experimental results demonstrate the effectiveness of the proposed approach with respect to existing algorithms.

On the Utilization of Spectrum Opportunity in Cognitive Radio Networks

Cognitive radio (CR) is proposed to use the spectrum opportunistically. Spectrum opportunity (SOP) can be defined as the possibility of a spectrum-aware communication between a CR transmitter and a CR receiver. Successful spectrum-aware communication between the communicating CRs, which is utilization of SOP (USOP), depends on the SOP detection and the correct transmission of a packet. Spectrum sensing performance, physical channel, and network parameters affect the probability of USOP. In this letter, we characterize the probability of the USOP under different network topologies. The network topology is due to the relation between transmission ranges of licensed users and CRs. We numerically study this probability for different network parameters and topologies. We find that the characteristics of USOP highly depend on the network topology, CR sensing performance and licensed users’ activities.

Detection and Leasing of Joint Space and Spectrum Opportunities by Multiple Secondary Network Operators in Cognitive Radio Systems

The fifth generation (5G) wireless networks are expected to achieve 1000 times higher capacity compared to the fourth generation (4G) wireless networks. This implies that maximizing the spectrum efficiency will be one of the key issues to 5G systems. In this paper we consider the problem of detecting and leasing the joint space and spectrum opportunities by a number of secondary network operators (SNOs) in a cognitive network. With the exploitation of an antenna array equipped at each SNO, the whole space in the view of the array can be sliced by multiple partially overlapped beams for each narrowband channel. We derive the energy detection probability of the primary users (PUs) at a given beam for each narrowband channel and the probabilities of that the PUs are located respectively at each neighboring beam under the condition of the PUs located at the given beam and the given narrowband channel. To maximize the spectrum utility, we propose to model the spectrum leasing market of the cognitive network for the obtained opportunities in the framework of the differential games, such that the spectrum price and demand are controlled by a dynamic system. We then derive the open-loop equilibria of the game. Simulation results confirm the efficacy of the proposed methods.

A tree-structured DFT filter bank based spectrum detector for estimation of radio channel edge frequencies in cognitive radios

Cognitive Radio (CR) aims to provide efficient spectrum utilization in spectrum scarce wireless environments. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor the electromagnetic spectrum and detect unused bands of spectrum. Wideband spectrum sensing needs to be employed for better spectrum opportunity detection and interference avoidance both in the case of commercial and military applications. Accurate sensing needs to be employed for blocker detection in commercial systems such as LTE for the design of transmit/receive path. In military radios, the challenge lies in the robust detection of the location of the center frequencies and bandwidths of individual radio channels in the wideband input signal. In this paper, an energy detector based on tree-structured discrete Fourier transform based filter bank (TDFTFB) is proposed for detecting the edges of the channels in the spectrum. The proposed method is compared with the conventional wavelets based method for complexity and performance. The design example and simulations show that the gate count resource utilization of the proposed detection scheme is 22.9% lesser than the wavelets method at the cost of a slight degradation (0.5%) in detection accuracy. Over-the-air tests performed using Universal Software Radio Peripheral 2 (USRP2) and MATLAB/SIMULINK showed that the present method is not input specific whereas the conventional wavelet based approach depends on the spectral location of the input.

Uplink User Signal Separation for OFDMA-Based Cognitive Radios

Spectrum awareness of orthogonal frequency division multiple access- (OFDMA-) based cognitive radios (CRs) can be improved by enabling them to separate the primary user signals in the uplink (UL). Assuming availability of information about the basic parameters of the primary system as well as time synchronization to the first arriving user signal, two algorithms are proposed in this paper. The first one targets estimating the size of the frequency allocation block of the primary system. The performance of this algorithm is compared with the results of a Gaussian approximation-based approach that aims to determine the probability of correct block size estimation theoretically. The second one is a semiblind user separation algorithm, which estimates the carrier frequency offsets and time delays of each block by exploiting the cross-correlations over pilot subcarriers. A two-dimensional clustering method is then employed to group the estimates, where each group belongs to a different user. It is shown that the proposed algorithms can improve the spectrum opportunity detection of cognitive radios. Feasibility of the algorithms is proved through practical simulations.

Opportunity detection for OFDMA-based cognitive radio systems with timing misalignment

Accurate detection of spectrum opportunities within the frequency band of an orthogonal frequency division multiple access (OFDMA) system carries critical importance for OFDMA-based cognitive radios. In this paper, we analyze the opportunity detection performances of energy detection and ESPRIT (estimation of signal parameters by rotational invariance techniques) algorithms in the presence of timing misalignments in uplink (UL) OFDMA. For the energy detector, the statistics of subcarrier power are derived considering timing misalignments, and they are verified through computer simulations. Using these statistics, which take inter-carrier-interference (ICI) effects into account, receiver operating characteristics (ROCs) of the energy detector receiver are obtained. It is shown that energy detection has a considerably better performance than ESPRIT, especially when the subcarrier assignment changes frequently. Moreover, a closed form expression is derived for the UL-OFDMA synchronization point that minimizes the ICI. Finally, it is shown that employing resource allocation blocks with larger sizes in the primary network yields better opportunities for the cognitive radio.

Proactive detection of spectrum opportunities in primary systems with power control

Spectrum sensing finds spectrum opportunities for cognitive radio (CR) and enables CR users to work without harmful interference to primary users. Most of existing contributions on spectrum sensing detect whether a primary signal is present or absent. Since the ultimate goal of spectrum sensing is to avoid interfering with primary receivers (PRs), it is more efficient to detect PRs directly. In this paper, we propose a proactive spectrum sensing scheme to detect whether a PR is within the coverage or the interference range of a CR transmitter by exploiting the close-loop power control that has been widely used in wireless systems. With the proposed scheme, the CR user may still be able to access the licensed spectrum band even though a primary signal is detected as long as its transmission does not interfere with the PR. As a result, more spectrum opportunities can be exploited compared to conventional spectrum sensing methods.