Cancellation techniques of co‐channel interference in asynchronous spread spectrum multiple access systems

Abstract

A spread spectrum multiple access (SSMA) communication scheme employing asynchronous code‐multiplexing technique has been used in both mobile and satellite communication systems. One shortcoming of the SSMA system is the co‐channel interference due to the correlations among the pseudo noise (PN) sequences being used by multiple channels. Such co‐channel interference in the reception limits the number of channels that can share one common frequency band. Furthermore, there is a distant‐dependent problem of different received‐powers from different transmitters. This paper describes a technique for cancelling the co‐channel interference in asynchronous SSMA systems. This technique does not treat the co‐channel interference as thermal noise. It considers the co‐channel interference as components that can be recreated at each receiver. If the correlations among the PN sequences of all the active channels become known, the co‐channel interference can be cancelled using the linear algebraic approach. As a result, a larger number of channels can share the same frequency band more efficiently. Further, if initial synchronization can be implemented then the distant‐dependent problem can be alleviated.

Decision feedback multiuser detection: a systematic approach

“…The coefficients of the linear combination are the nonzero elements of the th row of . The resulting decision statistic goes through a minimum Euclidean PAM slicer to produce the th user's decision according to (19) where is the PAM alphabet. The function of the feedforward vectors is to minimize the effects of multiple-access interference from the as yet undecoded ("future") users to , and the residual interference after subtraction from the already decoded ("past") users to .…”

“…Suboptimum multiuser detection based on interference cancellation from tentative decisions was introduced in the form of multistage detection by Varanasi and Aazhang in [34]- [37] (see also Kohno, Imai, and Hatori [19]). The two-stage detector makes a decision at the second stage on a user's symbol by estimating and then subtracting from that user's matched 0018-9448/99$10.00 © 1999 IEEE filter output, the multiple-access interference contributed by other users based on tentative decisions of the conventional (decorrelating) detector made at the first stage.…”

A Near-Maximum-Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming

“…Then, the equation for the components of x in (8) reduces to x = Su and the relationship for the MIMO system model is given in (7).…”

“…However, unfortunately, these algorithms usually result in a noticeable degradation in the performance. Examples of such polynomial time algorithms include: Zero Forcing Detector (ZFD) [6], [7], Minimum Mean Squared Error Detector (MMSED) [8], [9], Decision Feedback Detector (DFD) and Vertical Bell Laboratories Layered Space-Time Nulling and Cancellation Detector (VBLAST Detector) [10], [11].…”

Successive Interference Cancellation Using Constellation Structure

“…Among the many suboptimal lower complexity alternatives to maximum-likelihood detection, the sequential decoding algorithm [17], cyclic decision feedback sequence detection [18], group detection [19][20][21][22][23] and detection based on interference cancellation from tentative decisions [12,[24][25][26][27] deserve mention. An extensive literature on multi-stage detection can be found in [11,[28][29][30][31][32] and the references therein.…”