Diversity Combining and SNR Estimation for Turbo-Coded Frequency-Hopped Spread-Spectrum in Partial-Band Interference and Fading Channels

Abstract

We compare different combinations of the repetition diversity order L and code rate R for turbo-coded Frequency-Hopped Spread-Spectrum (FH/SS) communication systems in the presence of fading and partial-band Gaussian interference. For a fixed overall channel code rate R/L we show that using the lowest code rate and no repetition diversity always performs better than using a higher code rate and some repetition for both coherent and non-coherent schemes. We then propose a simple maximum-likelihood-based method for signal-to-noise-ratio (SNR) estimation in Non-Coherent Binary Frequency Shift Keying (NCBFSK) without training symbols. Except for impractically small hop sizes of 8 bits or less we obtain performance virtually equal to that of perfect SNR knowledge but with much less complexity than iterative schemes previously proposed. For the case of Coherent Binary Phase Shift Keying (CBPSK) we derive the Expectation Maximization (EM) estimate of the SNR without training symbols and iteratively feed the estimator with the extrinsic information from the turbo decoder. The performance for CBPSK is near that of perfect SNR knowledge for hop sizes of 64 bits or more. Unlike previously proposed methods for CBPSK the EM estimate of SNR does not require knowledge of the noise and interference variance, received bit energy, or the fading channel model.

Signal-to-noise ratio estimation for M-QAM signals in η−μand κ−μfading channels

“…and the SNR can be written as (5) in which σ 2 g = E |g[ n] | 2 represents the average power of the fading, M represents the order of the QAM constellation, and |A i | and p i represent the amplitude and probability, respectively, of the occurrence of the ith symbol.…”

“…In [3], the knowledge of the SNR is necessary for assessing the time-varying channel condition of an adaptive system with frequency hopping, and in [4] it is necessary for planning relay communication systems. In [5], SNR is a useful parameter in the scenario of turbo decoding systems, while in [6] it is useful in the context of low-density parity check (LDPC) codes.…”

Iterative MMSE estimation of SINR for turbo‐coded FH‐SS systems in partial‐band interference and fading channels

“…The number of iterations used is 10. The channel interleaver is a simple matrix interleaver whose size is made equal to the coded turbo frame size as in Reference [4]. For comparisons with the MAP-based method of Reference [2], the number of regions M in the Lloyd-Max quantiser is set to 8, as in Reference [2].…”

“…Unlike the EM estimate we derived in Reference [4], the MMSE estimate obtained in this paper requires a priori statistics on fading and interference. We again use an iterative technique in which the turbo decoder extrinsic information is fed back to the SINR estimator.…”

“…However, no interference was assumed there either. In Reference [4], the EM estimate of the SINR was obtained and had the advantage of not requiring any information on the statistics of fading or interference. Except for large hop sizes, its performance for BPSK leaves room for improvement compared to the case with perfect SINR knowledge.…”