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In the presence of narrowband interference, non-Wiener effects have been observed for the normalized least-mean-square (NLMS) equalizers. These effects can lead to performance improvements over the fixed Wiener filter with the same model structure, but the convergence rate of the NLMS equalizer slows down seriously. In this paper, the steady-state and(More)
Traditional spectrum management mode results in spectrum scarcity and deployment difficulty and new spectrum management is desiderated. To overcome this problem, autonomous spectrum management system based on viable system model is proposed. It decouples policy from behavior and protocol in order to reduce the deployment complexity, and exploits viable(More)
Cognitive Radio (CR) is a revolution in wireless communication and enables flexible spectrum assess. This paper addresses the topology management problem in cognitive radio Ad Hoc networks (CRAHNs). Distributed multihop architecture, mobility of CR users, spectrum availability variance in different time and location are some of the key distinguish(More)
Based on discrete sampling model of differential frequency hopping (DFH) received signal, optimal noncoherent frequency detection of DFH signal in additive white Gaussian noise(AWGN) has been provided. According to DFH principle, bit error ratio (BER) performance of DFH is achieved. Comparing simulation results with theoretical analysis, they have(More)
As a frequency hopping communication system with memory, the Hidden Markov Model (HMM) of differential frequency hopping (DFH) is presented firstly, then a frequency sequence estimation algorithm based on minimum sequence error (MSE) criteria is proposed, in order to evaluate the algorithm, we compare this frequency sequence estimation method with(More)
A novel adaptive algorithm for blind separation of the linearly mixed signals based on the relationship of the variability between the source and the mixed signals is presented, which utilizes the generalized eigen-subspace decomposition based on the recursive least square (RLS) parallel computation. The presented algorithm has less mean square error by(More)
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