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A compromise between sensing performance and overhead in cluster-based spectrum sensing for cognitive radio networks, is still a critical issue. This matter has attracted the attention of many researchers to tackle the trade-off between the performance and overhead. This paper proposes an energy efficient multi-level hierarchical structure algorithm using double fusion stages to minimize control overhead for a cluster with large number of sensors. An iterative algorithm is proposed to determine a suboptimal number of hierarchical levels. Optimal threshold, optimal fusion rule parameters and energy efficiency analysis are considered. The simulation results show that the proposed algorithm provides better detection performance, increases the throughput, and reduces considerably both the overhead and the reporting energy. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents new algorithms that exploit uncorrelation between adjacent data samples produced by IFFT in ADSL systems. These algorithms update time-domain equalizer (TEQ) coefficients by adjacent lag autocorrelation minimization (ALAM) to shorten the channel. ALAM is an addition to the family of several existing autocorrelation-based algorithms that can achieve similar or better performance at lower complexity compared to other algorithms. The complexity reduction is achieved by designing a cost function without the sum-square, and further utilizing symmetrical TEQ property to reduce the complexity of the adaptation of TEQ to half of other existing algorithms. Furthermore, to avoid the limitations of lower bitrate after reaching the maximum bitrate and high complexity, an adaptive TEQ using equal-taps constraint (ETC) is introduced to maximize the bitrate along with achieving the lowest complexity. In addition, a simplified approach is used to calculate the expectation to reduce the computational complexity for ALAM and the other existing autocorrelation-based algorithms. Simulation results demonstrate that the proposed algorithms and modified existing algorithms achieve similar or better performance figures with reduced complexities. [Copyright &y& Elsevier]

Abstract: This paper discusses the design and field programmable gate array (FPGA) implementation of a configurable 18-tap fractionally-spaced blind adaptive equalizer intellectual property (IP) core for quadrature amplitude modulation (QAM) signals. The design can be configured to implement the constant modulus algorithm (CMA), multimodulus algorithm (MMA), radius-adjusted modified-multimodulus algorithm (RMMA), and radius-adjusted multimodulus decision-directed algorithm (RMDA), while it can achieve channel equalization for square QAM signals up to 256-QAM. The input samples to the equalizer tapped delay line are sampled at twice the symbol rate, while the equalizer output and tap coefficients are updated at the symbol rate. This is exploited by the equalizer tap and update modules of the design, which utilize the same hardware to implement two consecutive equalizer taps per module. The IP core is implemented for the Altera Stratix II EP2S130F780C4 FPGA and targets cable demodulators. The implementation operates at a maximum symbol frequency of 8.055 MBaud, which is comparable to recent QAM equalizer designs for cable modems. [Copyright &y& Elsevier]