A subspace approach to blind space-time signal processing for wireless communication systems

Abstract

The two key limiting factors facing wireless systems today are multipath interference and multiuser interference. In this context, a challenging signal processing problem is the joint space-time equalization of multiple digital signals transmitted over multipath channels. We propose a blind approach that does not use training sets to estimate the transmitted signals and the space-time channel. Instead, this approach takes advantage of spatial and temporal oversampling techniques and the finite alphabet property of digital signals to determine the user symbol sequences. The problem of channels with largely differing and ill-defined delay spreads is discussed. The proposed approach is tested on actual channel data. I. INTRODUCTION A challenging problem in signal processing is the blind joint space-time equalization of multiple digital signals transmitted over multipath channels. An important area where such a problem arises is wireless (mobile) communications. Consider a scenario where several users are trying to talk to a central base station, which has several antennas (viz., Fig. 1). A space-time equalizer at the base station combines two signal processing aspects: equalization (or echo canceling) to combat the intersymbol interference due to large-delay multipath and source separation to combat cochannel interference (CCI). The CCI might be interfering signals at the same frequency from neighboring communication cells, or we might intentionally allow multiple users at the same frequency in order to increase the system capacity. The latter is known as space division multiple access (SDMA) because it essentially separates users based on differences in location. Current communication systems such as IS-54 and GSM require some amount of equalization (up to five symbol periods in GSM and up to one symbol period in IS-54) but are not designed to handle cochannel users. To assist "classical" single-user channel identification algorithms, a fair number of training symbols are incorporated in the data packets.

Blind synchronization in asynchronous multiuser packet networks using KMA

“…Algorithm 2 Another algorithm for subspace intersection is mentioned in [12]: the common vector in the column span ofPx andPy is given by the largest left singular vector of [ÛxÛy], the one corresponding to a singular value √ 2. Interestingly, this vector should have the structure…”

Bounds and algorithm for direction finding of phase modulated signals

“…The problem with this is that the complementary spaces can be highly dimensional. It was proven in [8] that, since the rows of V i form a minimal and orthonormal basis for row& i ' , the same result can be obtained by constructing the matrix V T in (4) and looking for the right singular vector corresponding to the largest singular values of V T . In the noise-free case it is equal to the vector in the intersection; with noise perturbations, we find a sequence that "best" fits all subspaces.…”

“…We first review the row-span method of [8]. The symbol estimates produced by this technique can be regarded as the outputs of linear equalizers, averaged across all equalization lags.…”

“…Categories 2 and 3 have the advantage of by-passing the channel estimation and inversion step, which in cases results in increased robustness. The direct symbol-estimation methods [8,9] have sometimes been called row-span methods as they exploit the row-span information of the data matrix to find the vector of unknown symbols. Following a seemingly different strategy, MRE techniques [5] estimate a collection of channel equalizers by forcing them to produce the same (unknown) output sequence, up to fixed equalization lags.…”

“…e.g. [2][3][4], the second considers the estimation of channel inverses (equalizers) [5][6][7], and the third attempts to recover the transmitted symbols directly from a (typically small) batch of output samples without resorting to channel/equalizer estimates [8,9]. Categories 2 and 3 have the advantage of by-passing the channel estimation and inversion step, which in cases results in increased robustness.…”