This paper introduces methods for reducing the table storage required for encoding and decoding with unstructured vector quantization (UVQ) or tree-structured vector quantization (TSVQ). Specifically, a low-storage secondary quantizer is used to compress the code vectors (and test vectors) of the primary quantizer. The relative advantages of uniform and nonuniform secondary quantization are investigated. A Linde-Buzo-Gray (LBG) like algorithm that optimizes the primary UVQ codebook for a given secondary codebook and another that jointly optimizes both primary and secondary codebooks are presented. In comparison to conventional methods, it is found that significant storage reduction is possible (typically a factor of two to three) with little loss of signal-to-noise ratio (SNR). Moreover, when reducing dimension is considered as another method of reducing storage, it is found that the best strategy is a combination of both. The method of secondary quantization is also applied to TSVQ to reduce the table storage required for both encoding and decoding. It is shown that by exploiting the correlation among the test vectors in the tree, both encoder and decoder storage can be significantly reduced with little loss of SNR--by a factor of about four (or two) relative to the conventional method of storing test vectors (or test hyperplanes).

The authors propose and analyze a stable random access algorithm which is a modification of the two-cell algorithm proposed by M. Paterakis (1984). The authors assume ternary feedback broadcast per slot, and in the presence of capture, identification of the captured packet by all the users in the system. It is also assumed that a successfully received signal transmission cannot be distinguished from a capture in the presence of multiple transmission. The performance of the algorithm is compared to an appropriate modification of J.I Capetanakis' (1979) dynamic algorithm. The proposed algorithm, and in contrast to the latter, it can be easily modified to operate in limited feedback sensing environments.>