Minimum probability of error demodulation for multipath OFDM-SDMA systems

Abstract

The two major challenges encountered in the design of high speed wireless networks are the impairments caused by the propagation channel and scarcity of the electromagnetic spectrum. Orthogonal frequency division multiplexing (OFDM) simplifies the former problem by transforming a frequency selective channel into a frequency flat one. On the other hand, spectral efficiency can be achieved by space division multiple access. These two techniques can therefore be combined to achieve reliable wireless communication at high data rates. The resulting system, however, suffers from co-channel interference.In an earlier paper we proposed a class of demodulators that effectively combat this interference. These demodulators are based on the principle of minimizing the error probability in linear demodulation of the received symbols. The demodulators show considerable improvement in BER compared to the MMSE and adaptive RLS demodulation techniques. These demodulators however assume a known channel at the receiver. In this paper we dispense with this assumption by developing a training based adaptive channel estimator which is then coupled with these demodulators. The demodulators still show considerable gain in performance over the MMSE and RLS demodulators even with the estimated channel.Index Terms-OFDM, SDMA, co-channel interference, probability of error, antenna diversity, MMSE, RLS.

MPOE Based Prefiltering and MRT Beamforming with Matched Filter Receiver for DS-CDMA Systems

“…Dua and Desai in [14] first proposed the MPOE optimization method for general DS-CDMA system. Later Dua et al in [15], Sood and Desai in [16], Mohit Garg et al in [17] and Wang et al in [18] extended it to different scenarios. In [14][15][16][17][18] it was established that MPOE optimization has better performance than MMSE optimization.…”

“…Later Dua et al in [15], Sood and Desai in [16], Mohit Garg et al in [17] and Wang et al in [18] extended it to different scenarios. In [14][15][16][17][18] it was established that MPOE optimization has better performance than MMSE optimization. Moreover in [16][17][18][19][20] a linear computational complexity MPOE (with respect to the number of users) were proposed.…”

“…In [14][15][16][17][18] it was established that MPOE optimization has better performance than MMSE optimization. Moreover in [16][17][18][19][20] a linear computational complexity MPOE (with respect to the number of users) were proposed. In [27], Ding et al proposed a precoder and in [28] Palomar et al derived a transceiver based on minimum BER method for Zero-Forcing (ZF) equalizer at the receiver.…”

“…Usually MPOE optimization tends to be computationally expensive but since we are assuming ample computational resources at the base station, using MPOE instead of MMSE as the optimization criterion for prefiltering can be justified. Moreover, by conditioning the filter weights on the transmitted bits, one can design a MPOE detector with linear complexity [11][12][13][14][15][16][17][18]. In the second part of the paper, we consider MRT beamforming by taking advantage of the available channel knowledge for improving the bit error rate performance of the proposed prefilter models.…”

“…We believe that since the symbols are of significance for a digital communications system, the optimum prefilter should be the one which minimizes the probability of symbol error at the receiver [10][11][12][13][14][15][16][17][18]. We refer to such a system as the Minimum Probability of Error (MPOE) based system [14][15][16][17][18]. Usually MPOE optimization tends to be computationally expensive but since we are assuming ample computational resources at the base station, using MPOE instead of MMSE as the optimization criterion for prefiltering can be justified.…”