Cooperative Spectrum Sensing based on the Limiting Eigenvalue Ratio Distribution in Wishart Matrices

Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. Most of such techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far in this field are based on asymptotical assumptions, due to the difficulties in characterizing the exact distribution of the eigenvalues ratio. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then straightforwardly applied to calculate the decision threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial performance improvement compared to the other eigenvalue-based algorithms.Comment: 7 pages, 2 figures, submitted to IEEE Communications Letter

Guest Editorial: Channel Coding in Wireless Systems

It is our pleasure to introduce this Special Issue on "Channel Coding in Wireless Systems" of the Journal of Communication Software and Systems (JCOMSS). This is one of the initiatives conceived to promote JCOMSS, a relatively new journal born from an idea of the Croatian Communication and Information Society (CCIS) and the University of Split, and endorsed by the IEEE Technical Committee on Communications Software of the IEEE Communications Society. Really, the Croatian group are very active in supporting IEEE activities and proposing original ideas for exchanging experiences and results among researchers coming from universities, industries and research centers. Just to mention an example, it is the main organizer of SoftCOM, the International Conference on Software, Telecommunications and Computer Networks, that is probably the only ICT workshop in the world to be held aboard a ship, cruising along the Croatian and the Italian coasts. This year SoftCOM will celebrate its 14th edition. Then, charmed from so much dynamism, when Prof. Nikola Rozic asked for us to organize a special issue on the theoretical and practical aspects of channel coding for wireless applications, we accepted the invitation with great enthusiasm. Wireless systems are a privileged field to discuss the advantages of channel coding in telecommunications. The wireless channel is a particularly severe test-bed: it is affected by nonlinearities, multipaths, Doppler shifts, fading, shadowing, interference from other users, and many other impairments depicting an involved scenario, difficult to treat but also exciting for proposing new and attractive solutions. The invention of turbo codes in 1993 has been followed by the application of iterative techniques to many other blocks of communication systems, and the rapid implementation of these concepts in practical applications. In the last ten years new schemes have been designed (and old ones reinterpreted) able to approach Shannon capacity limits with reasonable complexity. This way, new applications and services become possible, quite unthinkable in the past

Performance Analysis of Multi-Antenna Hybrid Detectors and Optimization with Noise Variance Estimation

In this paper, a performance analysis of multi-antenna spectrum sensing techniques is carried out. Both well known algorithms, such as Energy Detector (ED) and eigenvalue based detectors, and an eigenvector based algorithm, are considered. With the idea of auxiliary noise variance estimation, the performance analysis is extended to the hybrid approaches of the considered detectors. Moreover, optimization for Hybrid ED under constant estimation plus detection time is performed. Performance results are evaluated in terms of Receiver Operating Characteristic (ROC) curves and performance curves, i.e., detection probability as a function of the Signal-to-Noise Ratio (SNR). It is concluded that the eigenvector based detector and its hybrid approach are able to approach the optimal Neyman-Pearson performance

Advanced channel coding for space mission telecommand links

We investigate and compare different options for updating the error correcting code currently used in space mission telecommand links. Taking as a reference the solutions recently emerged as the most promising ones, based on Low-Density Parity-Check codes, we explore the behavior of alternative schemes, based on parallel concatenated turbo codes and soft-decision decoded BCH codes. Our analysis shows that these further options can offer similar or even better performance.Comment: 5 pages, 7 figures, presented at IEEE VTC 2013 Fall, Las Vegas, USA, Sep. 2013 Proc. IEEE Vehicular Technology Conference (VTC 2013 Fall), ISBN 978-1-6185-9, Las Vegas, USA, Sep. 201

Performance Improvement of Space Missions Using Convolutional Codes by CRC-Aided List Viterbi Algorithms

Recently, CRC-aided list decoding of convolutional codes has gained attention thanks to its remarkable performance in the short blocklength regime. This paper studies the convolutional and CRC codes of the Consultative Committee for Space Data System Telemetry recommendation used in space missions by all international space agencies. The distance spectrum of the concatenated CRC-convolutional code and an upper bound on its frame error rate are derived, showing the availability of a 3 dB coding gain when compared to the maximum likelihood decoding of the convolutional code alone. The analytic bounds are then compared with Monte Carlo simulations for frame error rates achieved by list Viterbi decoding of the concatenated codes, for various list sizes. A remarkable outcome is the possibility of approaching the 3 dB coding gain with nearly the same decoding complexity of the plain Viterbi decoding of the inner convolutional code, at the expense of slightly increasing the undetected frame error rates at medium-high signal-to-noise ratios. Comparisons with CCSDS turbo codes and low-density parity check codes highlight the effectiveness of the proposed solution for onboard utilization on small satellites and cubesats, due to the reduced encoder complexity and excellent error rate performance

Application of List Viterbi Algorithms to Improve the Performance in Space Missions using Convolutional Codes

Currently, several space missions are still using convolutional codes, which are among the available coding options of the CCSDS telemetry recommendation. When convolutional codes are employed, the CCSDS specification mandates the use of an outer CRC code to perform error detection over the transfer frame. Alternatively, the CRC code may be used, together with list Viterbi decoding of the inner convolutional code, to significantly improve the performance of the coding scheme. In this paper, we first compute the distance spectrum of the concatenation of the outer CRC code and the inner convolutional codes recommended by the CCSDS. By means of a union bound on the block error probability under maximum-likelihood decoding, we estimate the extra coding gain achievable by the concatenation with respect to the use of the Viterbi algorithm applied to the decoding of the inner convolutional code only. The extra coding gain is close to 3 dB. Then, we consider the application of the list Viterbi algorithm and we discuss some techniques useful to reduce its complexity in practical implementations. Results show that it is possible to approach the 3 dB extra coding gain with negligible increase in the decoding complexity with respect to Viterbi decoding of the inner convolutional code

A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems

In this paper, we study 5G Channel State Information feedback reporting. We show that a Deep Learning approach based on Convolutional Neural Networks can be used to learn efficient encoding and decoding algorithms. We set up a fully compliant link level 5G-New Radio simulator with clustered delay line channel model and we consider a realistic scenario with multiple transmitting/receiving antenna schemes and noisy downlink channel estimation. Results show that our Deep Learning approach achieves results comparable with traditional methods and can also outperform them in some conditions