Efficient Estimation and Compensation of Transceiver Non-Reciprocity in Precoded TDD Multi-User MIMO-OFDM Systems

Abstract

In this paper, we propose an efficient transceiver non-reciprocity estimation-compensation framework for precoded TDD multi-user MIMO-OFDM downlink transmission systems. General signal models are first developed to analyze the effects of transceiver non-reciprocity at both the base-station and user equipment sides. The analysis shows that transceiver nonreciprocity at the base-station causes inter-user interference and thus substantial performance degradation, while the impact of transceiver non-reciprocity at the user equipment side can be fairly easily handled with downlink detector processing. Next, an overthe-air (OTA) type pilot-based estimation algorithm is devised for efficient identification of base-station non-reciprocity parameters, which are then used in pre-compensating or precoding the multiuser data properly in the base-station. Compared to the existing work in the literature, the proposed approach does not require the use of feedback signaling or complicated channel matrix decomposition techniques for extracting the non-reciprocity parameters. The resulting link and system performance of the proposed estimation-compensation framework is then evaluated using extensive computer simulations in linear precoded TDD multiuser MIMO-OFDM system context. Based on the obtained results, the proposed estimation-compensation approach can provide a simple, practical and flexible solution to efficiently restore the channel reciprocity with reasonable calibration overhead.

Estimation and Mitigation of Channel Non-Reciprocity in Massive MIMO

“…where the processed noise matrix is given by V =Z * tot X H nrc . Now the target is to estimate A and B from (15) assuming that the BS has the uplink channel estimateĜ. In this respect, denoting the estimates at m'th iteration asÂ(m) andB(m), we propose the following iterative estimation framework: 1) Initialize,Â(0) = I K , obtain the estimateB(1).…”

“…In above, I K is used for initialization since the deviation matrix A in A = I K + A is in practice small. Notice that the processed received signal in (15) and the corresponding UL channel estimate are available at multiple parallel sub-carriers in an OFDM/OFDMA based radio system. Hence, the above iterative estimation scheme can be carried out in a per subcarrier manner as well.…”

“…3) Successively refine the estimates  and B by fixing the current value of one and solving for the other from (15). In above, I K is used for initialization since the deviation matrix A in A = I K + A is in practice small.…”

“…[10]- [13] which all assume the availability of downlink pilots in the UE side, we consider the appealing massive MIMO scenario in which there are no downlink pilots and thus UEs rely on the statistical properties of the beamformed channels to decode the received downlink signals [8], [18]- [21]. 4) We demonstrate the performance of our proposed scheme under imperfect uplink CSI, unlike other works which commonly rely on the perfect uplink CSI assumption [4], [11]- [13], [15].…”

“…In this respect, only [6] reports similar modeling, however, the proposed mitigation scheme in [6] is suitable mainly for small scale MIMO systems, e.g., 2-4 BS antennas. 2) We address the estimation and mitigation of the NRC sources of both the UE and BS sides, unlike many other works that address only BS side NRC, e.g., [2], [4], [10]- [13], [15]. As shown in [17], with popular assumption of not having downlink demodulation pilots, UE side NRC can be a major cause of performance degradation in multiuser massive MIMO systems, thus strongly motivating to incorporate such effects in the NRC estimation and mitigation processes.…”