Improved cooperative spectrum sensing using belief propagation algorithm and energy detection

This paper presents a method to alleviate the effects of primary user emulation attacks in cognitive radio (CR) networks. The proposed method uses a simplified belief propagation (BP) algorithm that is able to identify whether a transmitter is a legitimate primary user or an attacker. In a primary user emulation attack (PUEA) a transmitter impersonates a primary user (PU) in order to deceive secondary users (SU) into believing that a channel is occupied. As a result, secondary users must vacate the channel immediately. This paper presents a simplified belief propagation method as a defence strategy against primary user emulation attacks. In our method each secondary user examines an incoming signal from a transmitter and determines with a certain probability whether the transmitter is a legitimate user or not. This probability is known as the belief. The beliefs at each secondary user are reconciled and a final belief is compared to a predefined threshold. If the final belief is below the threshold, the user is identified as an attacker. If it is above the threshold, the user is deemed a primary user. This result is then propagated throughout the network so that all users on the network are aware of the attacker. In this paper, we present a method based on the belief propagation framework. The proposed method converges in a single iteration; this is the result of a redefined messaging protocol and a simplified belief equation. As a result, the computational complexity of the method is reduced significantly, while still maintaining a high level of accuracy.

With the increasing number of wireless users every day, cognitive radio serves as an approach to solve the spectrum crunch problem. Spectrum sensing serves as the heart of cognitive radio with it being able to decide if an unlicensed user is to be given access to a licensed band or not without causing interference. Various spectrum sensing techniques have been discussed in the literature. In this paper, energy detection is considered for spectrum sensing, which conventionally works on a fixed threshold without requiring any prior knowledge of the signal. It is found the performance of conventional energy detection falls in regions with noise uncertainty. In this paper, a dynamic double threshold scheme along with cooperative spectrum sensing at the fusion center is proposed. The dynamic threshold selection works on the parameter of noise uncertainty for practical cases by creating a noise variance history. Also, the fusion center uses a dynamic threshold to make a final decision compared to a fixed threshold for all the energy values lying between the two thresholds at the local nodes. A simulation model has been discussed to compare the proposed scheme with traditional energy detection and other detection schemes as well. A 20% improvement in probability of detection at −22 dB SNR and 0.5 probability of false alarm is achieved using the proposed scheme.

In this paper, we propose a novel frame structure for spectrum sensing in a network of secondary users, and optimize the sum throughput of the network under the constraint on the aggregate interference offered by the network to the primary user. We contrast its performance with the traditional Stop And Sense (SAS) frame structure, which involves the secondary users stopping transmissions and sensing for spectrum holes. We obtain near-optimal, easily computable solutions for the sensing duration and sensing thresholds that approximately maximize the secondary throughput under an aggregate interference constraint. Through Monte Carlo simulations, we present a detailed comparison of the two schemes, and characterize the operating regimes where each frame structure is preferable over the other. In general when switching latencies are not accounted for, we find that the proposed Simultaneous Sense and Access (SSA) frame structure performs similar to the existing SAS frame structure in terms of throughput for all values of primary signal SNR. When switching latencies are accounted for, the SSA frame structure offers significantly higher throughput gain for all values of primary SNR. Another advantage of this frame structure over a few other existing frame structures when employed in a network of secondary users is that it does not rely on the data in the previous slot to make decisions about the presence/absence of primary signal in the current slot which makes the frame structure ideal for an adhoc network.

A novel protocol for transparent and simultaneous spectrum access between the secondary user and the primary user in cognitive radio networks

The cache-enabled D2D network supports direct interconnection among users and can help to offload the data traffic through user caching. In this paper, we consider a reasonable caching strategy design in the cache-enabled D2D network to improve its performance. First we formulate an optimization problem aiming to minimize the average file download time of the users. Next we transform the original problem to a max-product form that can be distributed solved. Then we introduce the factor graph and the conventional belief propagation (BP) algorithm. Finally, we show how we design a BP algorithm with Randomized Gaussian Message Passing scheme and Weighting mechanism (BP-RW) to improve its convergence and performance. The distributed BP-RW algorithm can assign almost all computations to each user in the network, which will help to reduce the heavy computational overhead of the central controller. Meanwhile, simulation results show that the BP-RW algorithm performs better than the conventional BP algorithm, and performs very closely to the centralized greedy algorithm with much lower computational complexity.

In this paper, we develop a Belief Propagation (BP) algorithm for similarity computation to improve the recommendation accuracy of the neighborhood method, which is one of the most popular Collaborative Filtering (CF) recommendation algorithms. We formulate a probabilistic inference problem as to compute the marginal posterior distributions of similarity variables from their joint posterior distribution given the observed ratings. However, direct computation is prohibitive in large-scale recommender systems. Therefore, we introduce an appropriate chosen factor graph to express the factorization of the joint distribution function, and utilize the BP algorithm that operates in the factor graph to exploit the factorization for efficient inference. In addition, since the high degree at the factor node incurs an exponential increase in computational complexity, we also propose a complexity-reduction technique. The overall complexity of the proposed BP algorithm on a factor graph is linear in the number of variables, which ensures scalability. Finally, through experiments on the MovieLens dataset, we show the superior prediction accuracy of the proposed BP-based similarity computation algorithm for recommendation.

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a priori describing the statistical behavior of the wireless environment concerned. In addition, we propose a blind threshold adaptation method to guarantee a certain performance level in a BP-based distributed detection system. To clarify the points discussed, we design a novel linear-BP-based distributed spectrum sensing scheme for cognitive radio networks and illustrate the performance improvement obtained, over an existing BP-based detection method, via computer simulations.

Wireless communication systems are developing rapidly along with increasing communication needs and community activities. The system is required to maintain the quality of communication in the density and scarcity of spectrum problems. There are still many spectrum holes that are not used by licensed primary users (PUs), especially in areas with moderate and low usage density. One of the solutions to increase opportunistic spectrum access without disturbing the PU is cognitive radio (CR). The spectrum allocation decision is based on the detection probability parameter on the secondary user (SU), which responsively detects the presence of the PU signal. Cooperative sensing is used to increase the reliability of detection in real situations when there is the uncertainty of noise, fading, or shadowing. This paper observes the cooperative sensing method of PU signal on the CR based on energy detection. The improvement of sensing performance is presented based on the parameters of detection probability. The method was based on energy detection using single and cooperative sensing in hard fusion rule. The evaluation was carried out by analyzing the detection probability on the variance of the number of SUs involved and the signal-to-noise ratio (SNR) level. The results showed that the cognitive radio system cooperative sensing method was better than single sensing. Cooperative sensing appears to be faster in reaching maximum detection probability at a low SNR level. The increasing number of users involved in cooperative sensing improves the detection probability and sensing performance.

Full duplex Cognitive Radio (CR) achieves more spectral efficiency if it operates in listen and talk mode. It also facilitates to find multiple free available spectrum in a wide band and fast frequency handoff in the case the secondary user uses the frequency occupied by the Primary User (PU). Full duplex CR works using two RF chain with employing the sensing by a dedicated RF chain and transmitting the data with other RF chain. The main challenge involved in full duplex sensing is the self-interference problem, particularly for energy detector based spectrum sensing. Energy detector based spectrum sensing is widely used because of less computational complexity and PU signal knowledge is not required which is an essential feature in wideband spectrum sensing. This work proposes an SDR implementation of full duplex CR using energy detector with self-interference cancellation using Wiener filter. The self-interference signal is predicted by Wiener filter and it is removed in the received signal before apply to the energy detector. The proposed scheme is simulated and validated through an experimental study on SDR platform. The results show the improvement in probability of detection of PU signal. Parallel and pipelined architectures are employed to implement the entire system using the Kintext 7 FPGA on NI USRP RIO 2943R SDR, which result in reduction of sensing time.

Severe intersymbol interference (ISI) is one of the main obstacles for reliable signal reception in ATSC DTV systems. Decision feedback equalizers (DFEs) are commonly used to suppress the ISI. However, DFEs may suffer from error propagation due to incorrect symbol decisions from the symbol slicer. This phenomenon deteriorates the performance even more when the post-cursor ISI is strong. In order to reduce error propagation, we present a novel hybrid equalization scheme for ATSC channels. The proposed scheme consists of an adaptive decision feedback sparsening filter (DFSF), and an iterative maximum a posteriori (MAP) equalizer based on the belief propagation (BP) algorithm. In the first stage, instead of removing all the ISI from post cursors, the DFSF employs a modified feedback filter which leaves the strongest post-cursor ISI taps uncorrected. As a result, a long ISI channel is equalized to a sparse channel having only a small number of nonzero taps. In the second stage, a belief propagation algorithm is applied to mitigate the residual ISI. Since the channel is typically time-varying and suffers from Doppler fading, the DFSF is adapted using the least mean square (LMS) algorithm, such that the amplitude and the locations of the nonzero taps of the equalized sparse channel appear to be fixed. As such, the channel appears to be static during the second stage of equalization which consists of the BP detector. Simulation results demonstrate that the proposed scheme outperforms the traditional DFE in symbol error rate, under both static channels and dynamic ATSC channels.

In this paper, we propose two novel generalized belief propagation (BP) algorithms to improve the convergence behavior of the conventional BP algorithm. By incorporating a dynamic temperature into the free energy formulation, message passing is performed on a dynamic surface of energy cost. The proposed cooling process helps BP converge to a stable fixed point with a lower energy value that leads to better estimations. For decoding turbo-like error correcting codes, we adopt a parametric Gaussian approximation to relax the binary parity check constraints and generalize the conventional binary networks as well. Both the computational complexity and the convergence rate of the proposed annealed BP algorithms are almost the same as those of the conventional BP algorithm. Simulated performance of the proposed algorithms when they are used to decode a low density parity check (LDPC) code and the (23,12) Golay code is presented to validate our proposals.

A cross-layer framework for resource constrained dynamic distributed spectrum sensing and scheduling is presented. A network of secondary users (SUs) opportunistically communicate over portions of the spectrum estimated to be unused by other systems. A central controller (CC) schedules the traffic of the SUs, based on distributed compressed measurements collected by the SUs. Sensing and access are jointly controlled to maximize the SU throughput, with constraints on PU throughput degradation and SU cost. Sparsity in the network dynamics is exploited: leveraging a prior spectrum occupancy estimate, the CC needs to estimate only a residual uncertainty vector via sparse recovery techniques. The trade-off between achieving accurate spectrum estimates, high throughput, and low state information overhead, is optimized via dynamic programming.

This work is directed towards a symbiotic architecture called Cognitive Relaying with Time Incentive (CRTI) for multiple Primary Users (PUs) in a Cognitive Radio network. The rationale of CRTI is that the PU with a weak transmission link seeks cooperation from the cognitive Secondary User (SU) nodes in its vicinity, and in return rewards them with an incentive time for their own communication. Since each PU has its own distinct bandwidth of operation, it is crucial to decide the most favorable use of the spectrum when relaying through the SU network. We propose two schemes-Band-Restricted Relaying and All-Band Relaying to achieve this objective. Cross-layer optimization problems are formulated for the relaying schemes which create the time incentive, as well as for the SUs to efficiently access this incentive. The proposed schemes are compared, and simulation results are furnished to demonstrate their differences and individual merits from the PU and SU's perspective.

We proposed two improved decoding algorithms for Low-Density Parity-Check (LDPC) codes based on the Belief-Propagation (BP) algorithm combined with Genetic Algorithm (GA). After giving a genetic interpretation of Tanner graph, GA is adopted to efficiently use the information passing from the variable nodes to the check nodes. Simulation results assert the superiority of our proposed algorithms over the BP algorithm both in BER (Bit Error Rate) and FER (Frame Error Rate). At last, optimization of the key parameter of the developed algorithms is given.

Cognitive radio technology enables spectrum sensing (SS), which allows the secondary user (SU) to access vacant frequency bands in the periods when the primary user (PU) is not active. Due to its minute implementation complexity, the SS approach based on energy detection (ED) of the PU signal has been analyzed in this paper. Analyses were performed for detecting PU signals by the SU in communication systems exploiting multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission technology. To perform the analyses, a new algorithm for simulating the ED process based on a square-law combining (SLC) technique was developed. The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems. The influence of a false alarm on the detection probability of PU signals impacted by operating parameters such as the signal-to-noise ratios, the number of samples, the PU transmit powers, the modulation types and the number of the PU transmit and SU receive branches of the MIMO-OFDM systems have been analyzed in the paper. Simulation analyses are performed by running the proposed algorithm, which enables precise selection of and variation in the operating parameters, the level of noise uncertainty and the detection threshold in different simulation scenarios. The presented analysis of the obtained simulation results indicates how the considered operating parameters impact the ED efficiency of symmetric and asymmetric MIMO-OFDM systems.