Ayse Kortun

On the Performance of Eigenvalue-Based Cooperative Spectrum Sensing for Cognitive Radio

In this paper, the distribution of the ratio of extreme eigenvalues of a complex Wishart matrix is studied in order to calculate the exact decision threshold as a function of the desired probability of false alarm for the maximum-minimum eigenvalue (MME) detector. In contrast to the asymptotic analysis reported in the literature, we consider a finite number of cooperative receivers and a finite number of samples and derive the exact decision threshold for the probability of false alarm. The proposed exact formulation is further reduced to the case of two receiver-based cooperative spectrum sensing. In addition, an approximate closed-form formula of the exact threshold is derived in terms of a desired probability of false alarm for a special case having equal number of receive antennas and signal samples. Finally, the derived analytical exact decision thresholds are verified with Monte-Carlo simulations. We show that the probability of detection performance using the proposed exact decision thresholds achieves significant performance gains compared to the performance of the asymptotic decision threshold.

Distribution of the Ratio of the Largest Eigenvalue to the Trace of Complex Wishart Matrices

This correspondence investigates the statistical properties of the ratio <i>T</i> = λ₁/Σ_i=1^mλ<i>i</i> , where are λ₁ ≥ λ₂ ≥ ··· ≥ λ_m the <i>m</i> eigenvalues of an <i>m</i> × <i>m</i> complex central Wishart matrix <b>W</b> with <i>n</i> degrees of freedom. We derive new exact analytical expressions for the probability density function (PDF) and cumulative distribution function (CDF) of <i>T</i> for complex central Wishart matrices with arbitrary dimensions. We also formulate simplified statistics of <i>T</i> for the special case of dual uncorrelated and dual correlated complex central Wishart matrices (<i>m</i> = 2) . The investigated ratio <i>T</i> is the most important ratio in blind spectrum sensing, since it represents a sufficient statistics for the generalized likelihood ratio test (GLRT). Thus, the derived analytical results are used to find the exact decision threshold for the desired probability of false alarm for Blind-GLRT (B-GLRT) detector. It is shown that the exact decision threshold based B-GLRT detector gives superior performance over the asymptotic decision threshold schemes proposed in the literature, which leads to efficient spectrum usage in cognitive radio.

On the Eigenvalue-Based Spectrum Sensing and Secondary User Throughput

In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with minimum eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for eigenvalue-based detectors such as maximum eigenvalue detection (MED) and maximum-minimum eigenvalue (MME) detection. Finally, the throughput is maximized using eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust eigenvalue-based techniques, which are insusceptible to noise uncertainty.

On the Performance of Eigenvalue-Based Spectrum Sensing for Cognitive Radio

In this paper, the distribution of the ratio of extreme eigenvalues of complex Wishart matrix is studied in order to calculate the exact decision threshold as a function of the desired probability of false alarm for maximum-minimum eigenvalue (MME) detection method for multiple receiver collaborative spectrum sensing. Furthermore, the proposed exact formulation is simplified for the case of two receiver based collaborative spectrum sensing and with finite number of samples. In addition, an approximate closed form formula of the exact threshold is derived in terms of a desired probability of false alarm for a special case having equal number of receive antennas and signal samples. Finally, using Monte-Carlo simulations, we verify the estimated values of exact decision threshold and their approximated closed-form values. The probability of detection performance has been verified using the proposed exact decision thresholds achieving significant performance gains compared to the performance of the asymptotic decision threshold.

Complex random matrices and multiple-antenna spectrum sensing

In this paper, we study the eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. First, we study the extreme eigenvalue distributions of a complex Wishart matrix and then, in contrast to the asymptotic analysis reported in the literature, we derive the exact distribution of the test statistics of (i) maximum eigenvalue detector (MED) (ii) maximum-minimum eigenvalue (MME) detector and (iii) energy with minimum eigenvalue (EME) detector for finite number of samples (n) and finite number of antennas (m). These distributions are represented by complex hypergeometric functions of matrix argument, which can be expressed in terms of complex zonal polynomials. We also describe the method to compute these complex hypergeometric functions. Based on these exact distribution of the test statistics we find the exact decision thresholds as a function of the desired probability of false-alarms for MED, MME and EME. Simulation results show superior performance compared to the decision thresholds obtained from asymptotic (i.e, n,m → ∞) distributions.

Exact performance analysis of blindly combined energy detection for spectrum sensing

In this paper, we propose exact decision thresholds for “blindly combined energy detection (BCED)” in the case of multiple receiver collaborative spectrum sensing. As opposed to the decisions thresholds estimation based on an asymptotic analysis in the sense of large samples and/or large collaborative antennas presented in the literature, the proposed mathematical formulation can be used to calculate exact thresholds for finite number of samples and collaborative antennas. The proposed formulation is based on our recent progress in the exact eigenvalue distributions of complex Wishart matrices with finite sizes. Moreover, the proposed thresholds valid for both correlated and uncorrelated Gaussian noise cases. Finally, we show that the probability of detection performance with the proposed exact decision thresholds performs better than the performance achieved with the decision thresholds calculated based on the asymptotic analysis, thus validate the importance of this work.

Optimal Video Streaming in Dense 5G Networks With D2D Communications

Mobile video traffic and mobile devices have now outpaced other data traffic and fixed devices. Global service providers are attempting to propose new mobile infrastructures and solutions for high performance of video streaming services, i.e., high quality of experience (QoE) at high resource efficiency. Although device-to-device (D2D) communications have been an emerging technique that is anticipated to provide a massive number of mobile users with advanced services in 5G networks, the management of resource and co-channel interference between D2D pairs, i.e., helper-requester pairs, and cellular users (CUs) is challenging. In this paper, we design an optimal rate allocation and description distribution for high performance video streaming, particularly, achieving high QoE at high energy efficiency while limiting co-channel interference over D2D communications in 5G networks. To this end, we allocate optimal encoding rates to different layers of a video segment and then packetize the video segment into multiple descriptions with embedded forward error correction before transmission. Simultaneously, the optimal numbers of descriptions are distributed to D2D helpers and base stations in a cooperative scheme for transmitting to the D2D requesters. The optimal results are efficiently in correspondence with intra-popularity of different segments of a video characterized by requesters’ behavior, characteristic of lossy wireless channels, channel state information of D2D requesters, and constraints on remaining energy of D2D helpers and target signal to interference plus noise ratio of CUs. Simulation results demonstrate the benefits of our proposed solution in terms of high performance video streaming.

Throughput analysis using eigenvalue based spectrum sensing under noise uncertainty

The essential tradeoff between sensing capability and achievable throughput of the secondary network is one of the active research topics for researchers working on cognitive radio. In this paper, noise uncertainty which has a great impact on sensing methods is taken into account in the maximization of throughput using eigenvalue based spectrum sensing schemes. This issue has not been tackled in the throughput associated studies before. First, the theoretical and empirical distributions of the decision statistics and the detection performances for eigenvalue based sensing techniques are studied in the presence of noise uncertainty. The computed detection probabilities of maximum-minimum eigenvalue (MME) detector and maximum eigenvalue detector (MED) are compared with the most widely used energy detector (ED). Then, in the light of the obtained results, the throughput of the secondary network is maximized in order to find out the sensing duration for each scheme using multiple receive antennas. It is shown that, under low signal to noise ratio (SNR) regime, the designed sensing slot duration achieves the best sensing throughput tradeoff.

Robust Decorrelating Detector in Multi-Path Fading Under Impulsive Noise

In direct-sequence code-division multiple-access systems, impulsive noise leads to increased bit error rates and hence limits the performance. The main novel contribution of this paper is the improvement achieved on the decorrelating detector by the addition of a nonlinear clipper in order to eliminate impulsive components. The system is studied under multi-path fading with impulsive noise. It is assumed that the user signals have unequal powers (near/far effect) which make it challenging to determine the threshold in the clipper structure. These cases are studied and it is shown that the proposed robust receiver reduces the impact of impulsive noise by eliminating extreme amplitudes.

Performance of closed-loop power control in DS-CDMA systems in impulsive noise

Accurate power control is an essential requirement in the design of cellular direct sequence code-division multiple- access (DS-CDMA) systems. Effective power control will heavily impact the system capacity. Power control should be able to adjust the power levels of each transmitted signal so that all users will maintain the desired signal-to-noise ratio. In this paper, performance of closed-loop power control is studied in DS-CDMA systems. The performance of the single user and the decorrelating detector by using closed- loop power control algorithm is shown under various channel conditions. The results show that, closed-loop power control improves the performance of decorrelating detector in AWGN and impulsive noise.