Performance analysis of diversity combining for frequency-hop communications under partial-band and multitone interference

• Main Author: Li, Gang
• Other Authors: Wang, Q.
• Format: Others
• Language: English; en
• Published: 2018
• Subjects: Spread spectrum communications; Frequency spectra
• Online Access: https://dspace.library.uvic.ca//handle/1828/9580

This dissertation is concerned with performance analysis of diversity combining schemes in frequency-hop spread spectrum communications under the worst case partial-band noise and multitone jamming. Performance of a ratio-threshold diversity combining scheme in fast frequency hop spread spectrum systems with M-ary frequency shift keying modulation (FFH/MFSK ) under partial-band noise (PBN) and band multitone jamming without and with the additive white Gaussian noise (AWGN) is analyzed. The analysis is based on exact bit error probabilities, instead of bounds on the bit error probabilities. A method to compute the bit error probability for ratio-threshold combining on jamming channel is developed. Relationship between the system performance and the system parameters, such as ratio-threshold, diversity order, and thermal noise level, is illustrated. The performances under band multitone jamming and under partial-band noise jamming are compared. For binary FSK modulation, the performance under the two types of jamming is almost the same, but for 8-ary FSK modulation, tone jamming is more effective against communications. The structure of the combiner is very simple and easy to implement. Another merit of this combiner is that its output can be directly fed to a soft-decision FEC decoder. Maximum-likelihood diversity combining for an FFH/MFSK spread spectrum system on a PBN interference channel is investigated. The structure of maximum likelihood diversity reception on a PBN channel with AWGN is derived. It is shown that signal-to-noise ratio and the noise variance at each hop have to be known to implement this optimum diversity combining. Several sub-optimum diversity combining schemes, which require the information on noise variance of each hop to operate, are also considered. The performance of the maximum-likelihood combining can be used as a standard in judging the performance of other suboptimum, but more practical diversity combining schemes. The performance of the optimum combining scheme is evaluated by simulations. It is shown that the Adaptive Gain Control (AGC) diversity combining actually achieves the optimum performance when interference is not very weak. But the performance difference between some of the known diversity combining schemes, which do not require channel information to operate, and the optimum scheme is not small when the diversity order is low. An error-correction scheme is proposed for an M-ary symmetric channel characterized by a large error probability Pe. Performance of the scheme is analyzed. The value of Pe can be close to, but smaller than, 1 – 1/M for which the channel capacity is zero. Such a large Pe may occur, for example, in a jamming environment. The coding scheme considered consists of an outer convolutional code and an inner repetition code of length m which is used for each convolutional code symbol. At the receiving end, the m inner code symbols are used to form a soft-decision metric, which is subsequently passed to a soft-decision decoder for the convolutional code. Emphasis is placed on using a binary convolutional code due to the consideration that there exist commercial codecs for such a code. New methods to generate binary metrics from M-ary (M > 2) inner code symbols are developed. For the binary symmetric channel, it is shown that the overall code rate is larger than O.6R0, where R0 is the cutoff rate of the channel. New union bounds on the bit error probability for systems with a binary convolutional code on 4-ary and 8-ary orthogonal channels are presented. Owing to the variable m which has no effect on the decoding procedure, this scheme has a clear operational advantage over some other schemes. For a BSC and a large m, a method presented for BER approximation based on the central limit theorem. === Graduate