Iterative receiver concept for TDMA packet data systems

Abstract

SUMMARYIn this paper we present an iterative receiver concept that improves the radio link performance of TDMA mobile communication systems. We consider sub-optimal receiver structures comprising of channel estimator, detector and channel decoder, where the performance is improved by iterative data processing among the receiver blocks. As a practical example, we consider packet data transmission in GSM and Enhanced Data rates for Global Evolution (EDGE). Low-complexity soft-in-soft-out (SISO) equalizers for EDGE are introduced and its modifications suitable for iterative detection in EDGE are derived. Application of iterative detection and channel estimation techniques in GSM/EDGE shows a significant performance improvement. Furthermore, we show that retransmission schemes specified for EDGE also benefit from iterative data processing.

Interference‐tolerating transmission protocol design for three‐cell systems

“…The previous scenario considered for CoMP can be treated as one extreme that assumes all of the other base stations' interference arrive at the mobile terminal. The other extreme is the time division multiple access (TDMA) protocol that cancels the interference by exhausting more time slots as in . The multi‐antenna technique exploits spatial diversity via cooperations amongst the base stations (BSs), and the TDMA protocol makes use of the orthogonal time slots.…”

Reduced Complexity EGPRS Equalizer for Base Station DSP

“…The core of the Reduced State Maximum-A-Posteriori Symbol-by-Symbol Estimator (RSMAPSSE) is the MAPSSE which generates MAP quality soft outputs with a RSSE low complexity range (through forward-backward recursion in the trellis). The prefilter focuses the energy in the first taps of the CIR for the MAP forward pass but not for the backward pass [11]. It can lower the performance of the algorithm therefore the metrics computed in the forward recursion are stored for the backward recursion.…”

“…/3(sn) are the probabilities to reach the state Sn, considering the received samples sequence rmn[k], k=n.. D -1 where D is the total length of the sequence to estimate. Using the two sets of metrics, we can compute log a posteriori probability ratio (LAPPR) as defined in [11] for the j-th bit of the n-th symbol received with:…”