Reporting Channel Errors Research Articles

Cooperative wideband spectrum sensing under reporting channel errors: Analysis and algorithm

This paper focuses on the performance analysis and the algorithm development for cooperative wideband spectrum sensing (CWSS) under imperfect reporting channels for cognitive radio (CR). The centralized approach with multiple distributed cooperating secondary users (CSUs) and a fusion center (FC) is considered for CWSS. Nakagami fading is used to model the channel between PUs and the CSUs. The CSUs equipped with multiple diversity antennas are considered to take advantage of both space and antenna diversity. In this work, the reporting channel model under erroneous reporting is proposed. The repetition code-based CWSS (RC-CWSS) algorithm is proposed for performance improvement. First, the modified, improved theoretical analysis of an existing algorithm called partial band Nyquist sampling-based CWSS (PBNS-CWSS) under no reporting errors is given. The theoretical analysis under an imperfect reporting channel is then carried out for the same algorithm. Then, the complete theoretical analysis for the proposed RC-CWSS algorithm is carried out. The theoretical analyses are verified using Monte-Carlo simulations. The analysis shows that the performance of CWSS is greatly affected by the reporting channel errors. Also, the RC-CWSS outperforms recently proposed state-of-the-art algorithms. Finally, the effects of different parameters on the performance of the proposed algorithm are also studied.

Soft-Decision-Based Distributed Detection With Correlated Sensing Channels

Prior works on distributed detection with correlated Gaussian observations primarily dealt with single-bit reports to the fusion center and suboptimal fusion rules. Considering multiple bits and independent reporting channel errors, we formulate the optimal detection problem as a nonlinear integer programming problem for which the genetic algorithm is applied to find a suboptimal solution. The Bayesian probability of error performance of our suboptimal solution, first, for one-bit, is better than the performance of some previously proposed schemes and, second, for multiple bits, approaches that of the centralized detection scheme.

Multichannel Cooperative Spectrum Sensing That Integrates Channel Decoding With Fusion-Based Decision

This paper considers coded multichannel cooperative spectrum sensing (MC-CSS) employing local Neyman–Pearson testing at each sensor and channel decoding integrated with fusion-based decision. A joint multichannel decoding and decision fusion (JMCDDF) algorithm for performing a likelihood ratio test at the fusion center is derived based on the belief propagation technique. For benchmark comparison, we also derive the analytical performance of MC-CSS with error-free reporting channels that do not require channel coding, employing equal decision thresholds at each sensor. Using the JMCDDF algorithm, we compare the performance between uncoded and coded MC-CSS schemes when applying low-density parity-check (LDPC) codes in the presence of reporting channel errors. Monte–Carlo simulation results show considerable performance gains when using the proposed coded MC-CSS schemes. A 3-dB saving in link budget can be achieved by such coded MC-CSS schemes with a short (3, 6) regular LDPC code of codeword length $n_c=\text{200}$ , over uncoded MC-CSS. Finally, it is shown that in some cases, protecting the primary user (PU) channels by using variable nodes of higher degrees improves the performance. To equalize the performance for all sensed PU channels, we introduce a simple permutation technique, where, in each transmission, different variable nodes are used to protect local decisions.

Energy-Efficient Cooperative Spectrum Sensing With Reporting Errors in Hybrid Spectrum Sharing CRNs

Recently, cooperative spectrum sensing (CSS) in cognitive radio networks has been extensively researched. However, most of the studies mainly focus on maximizing the spectral efficiency while the energy consumption is generally ignored. However, since the secondary users (SUs) are usually battery-powered devices, energy saving is very important. This paper studies the mean energy efficiency (EE) maximization problem of the CSS system using the hybrid spectrum sharing (HSS) scheme. Specifically, the effects of imperfect spectrum sensing and reporting channel errors on the EE are considered. Our goal is to maximize the mean EE of SUs while maintaining the detection accuracy by jointly optimizing the sensing time and the number of cooperative SUs, subject to the SUs' average/peak transmission power constraints (ATPC/PTPC) and minimum data rate constraint, and the average interference power constraint of primary user. To address the non-convexity of the optimization problem, an energy-efficient iterative power allocation algorithm is developed. Simulations compare the achievable mean EE under ATPC/PTPC with three hard combining fusing rules using the HSS scheme and the opportunistic spectrum access (OSA) scheme, respectively; the results show that our proposed HSS scheme can obtain higher mean EE than the conventional OSA scheme and that the EE achieved under ATPC is better than that under PTPC. Moreover, among the three hard combining fusing rules, the Majority rule has the best EE.

An Innovative Cooperative Spectrum Sensing Algorithm with Non-ideal Feedback Channels and Delay Considerations

Cognitive radio (CR) is used to overcome the spectrum scarcity problem, which results from fixed allocation of wireless bands. CR allows the unlicensed secondary users to exploit the idle spectrum, which is not occupied by any licensed primary user (PU), thus increasing the overall spectrum utilization. In this paper, we first propose a simple cooperative sensing algorithm, which combines the local decision at each CR along with a group decision received from a fusion center to produce a collective decision on the existence of the PU. The performance of the algorithm is investigated over ideal and non-ideal reporting channels, from the fusion center to the CR devices, both analytically and via simulations. Furthermore, the effect of cooperation delay, which causes the decisions received by the CR device from the fusion center to be outdated, is extensively studied, both analytically and via simulations. To overcome the significant performance degradation due to the effect of delay, an extra local sensing cycle is performed at the CR side upon reception of the group decision. Results show that the proposed algorithm outperforms the conventional hard decisions technique and exhibits a comparable performance to the soft decisions approach at a considerably lower complexity. Moreover, the algorithm is shown to enjoy more robustness against reporting channel errors than the conventional hard decisions-based algorithm. Finally, the extra sensing cycle is shown to dramatically improve the performance for different delay scenarios.

BEP walls for cooperative sensing in cognitive radios using K-out-of-N fusion rules

Cooperative spectrum sensing is used to identify idle spectrum in cognitive radio or co-existing wireless systems. This paper identifies a performance limitation for cooperative sensing (CS) involving distributed secondary users (SUs) and a fusion center (FC). The local binary decisions are fused at the FC using a K-out-of-N fusion rule. The performance limitation presented in this paper for CS is in the form of a bit error probability (BEP) wall and results from imperfect reporting channels between the SUs and the FC. That is, if the BEP of the reporting channel exceeds the BEP wall value, then irrespective of the received signal quality on the listening channel or the sensing time at the SUs, constraints on the detector performance cannot be met at the FC. The BEP wall is an important phenomenon and needs to be taken into account while designing communication protocol between the SUs and the FC. Expressions for the BEP walls are derived in terms of the error probabilities at the FC and the number of users cooperating. Further, important properties of the BEP walls are derived and discussed in detail for the K-out-of-N fusion rules under the assumption of independent and identically distributed reporting channel errors.

Cooperative Sensing With Imperfect Reporting Channels: Hard Decisions or Soft Decisions?

This paper focuses on the performance analysis and comparison of hard decision (HD) and soft decision (SD) based approaches for cooperative spectrum sensing in the presence of reporting channel errors. For cooperative sensing (CS) in cognitive radio networks, a distributed detection approach with displaced sensors and a fusion center (FC) is employed. For HD based CS, each secondary user (SU) sends a one-bit hard local decision to the FC. For SD based CS, each SU sends a quantized version of a local decision statistic such as the log-likelihood ratio or any suitable sufficient statistic. The decision statistics are sent through channels that may cause errors. The effects of channel errors are incorporated in the analysis through the bit error probability (BEP). For HD based CS, the counting rule or the K-out-of-N rule is used at the FC. For SD based CS, the optimal fusion rule in the presence of reporting channel errors is derived and its distribution is established. A comparison of the two schemes is conducted to show that there is a performance gain in using SD based CS even in the presence of reporting channel errors. In addition, a BEP wall is shown to exist for CS such that if the BEP is above a certain value, then irrespective of the received signal strength corresponding to the primary user, the constraints on false alarm probability and detection probability cannot be met. It is shown that the performance of HD based CS is very sensitive to the BEP wall phenomenon while the SD based CS is more robust in that sense.