Decentralized widely linear precoding design for the MIMO interference channel

Abstract

Abstract-This 1paper addresses the interference management in a MIMO interference channel (MIMO-IC) by proposing a decentralized transmit and receive beamformer optimization using improper (or circularly asymmetric complex) Gaussian signaling. For the ease of exposition, the downlink (DL) of a cellular network is considered. In order to generate improper Gaussian signals, widely linear precoding (WLP) is adopted at transmission, while at reception we consider that users might apply either widely linear estimation (WLE) or linear estimation. The coordination between transmitters for WLP design is attained by taking into account the received signal in the uplink (UL), provided that propagation channel reciprocity can be assumed and that transmit filters in the UL are appropriately designed. In this way the estimation of the interfering channels is avoided, while we can take advantage of both DL transmit coordination to adjust transmit power and beamformers and the use of improper Gaussian signaling to exploit the real and imaginary dimensions of the MIMO channel. Simulations show that the proposed decentralized technique allows reducing the mean square error (MSE) and increasing user throughput in highly interfered scenarios.

On the Superiority of Improper Gaussian Signaling in Wireless Interference MIMO Scenarios

“…The exact rate improvement expression cannot be extracted, however, it is given by how much the eigenvalues in the improper interference case majorize the eigenvalues in the proper interference case, i.e. the difference in the majorization results in (17). Said difference becomes larger asC s A in (16) "increases" because the stronger the off-diagonal blocks in (16) are the more spread out the eigenvalues become (see Lemma 6 in Appendix A-B).…”

“…The weak log-majorization result in (17) of Lemma 1 is demonstrated in continuation by using some useful majorization theory properties that can be derived from (67). The first useful majorization theory property is [23,Sect.…”

“…The same applies in the Gaussian MIMO interference channel (IC), where IGS has been shown to: i) obtain larger degrees of freedom, i.e. slope of the sumrate as a function of the signal-to-noise ratio (SNR) at the high SNR regime, [3][5] [13] and ii) improve the known achievable rates when interference is treated as noise under certain MIMO configurations [4][14] [15] and under coexistence of linear and widely linear transceivers in MIMO systems [16] [17]. In addition, the positive benefits of IGS have been reported in terms of achievable rates [18] and outage probabilities [19] for underlay cognitive radio systems.…”

A Journey From Improper Gaussian Signaling to Asymmetric Signaling

“…A rate R is achievable if there exists a sequence of codes such that the maximal probability of error tends to 0 for sufficiently large block lengths. Recent contributions have highlighted a more general framework to quantify the achievable rate in order to accommodate the improper nature of the participating signals arising from IGS [9], [50], [52], [54], [55], [64], [82], [86], [88], [201]- [204], WL precoding of PGS [66], [71], [72], [79], [81], [84], [87], [95], [205], or complex beamforming resulting in dependent real and imaginary parts [90], [206].…”

“…1) Average Achievable Rate Limits: Average achievable rate or ergodic rate is an insightful performance metric to quantify the capability of a network to transmit the number of bits per second per Hertz. This section summarizes the average achievable rate performance gains of IGS over PGS in various interference limited scenarios arising in cognitive radio schemes [52], [54], [203], multi-antenna or MU systems [9], [87], [95], [205], and relay systems [64], [90], [202] etc.…”