Distributed CSI Acquisition and Coordinated Precoding for TDD Multicell MIMO Systems

Abstract

PostprintThis is the accepted version of a paper published in IEEE Transactions on Vehicular Technology. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.Citation for the original published paper (version of record):Brandt, R., Bengtsson, M. (2015) Distributed CSI Acquisition and Coordinated Precoding for TDD Multicell MIMO Systems. Abstract-Several distributed coordinated precoding methods exist in the downlink multicell MIMO literature, many of which assume perfect knowledge of received signal covariance and local effective channels. In this work, we let the notion of channel state information (CSI) encompass this knowledge of covariances and effective channels. We analyze what local CSI is required in the WMMSE algorithm for distributed coordinated precoding, and study how this required CSI can be obtained in a distributed fashion. Based on pilot-assisted channel estimation, we propose three CSI acquisition methods with different tradeoffs between feedback and signaling, backhaul use, and computational complexity. One of the proposed methods is fully distributed, meaning that it only depends on over-the-air signaling but requires no backhaul, and results in a fully distributed joint system when coupled with the WMMSE algorithm. Naïvely applying the WMMSE algorithm together with the fully distributed CSI acquisition results in catastrophic performance however, and therefore we propose a robustified WMMSE algorithm based on the well known diagonal loading framework. By enforcing properties of the WMMSE solutions with perfect CSI onto the problem with imperfect CSI, the resulting diagonally loaded spatial filters are shown to perform significantly better than the naïve filters. The proposed robust and distributed system is evaluated using numerical simulations, and shown to perform well compared with benchmarks. Under centralized CSI acquisition, the proposed algorithm performs on par with other existing centralized robust WMMSE algorithms. When evaluated in a large scale fading environment, the performance of the proposed system is promising. IEEE Transactions on Vehicular Technology

Fast-convergent distributed coordinated precoding for TDD multicell MIMO systems

“…The local CSI in the downlink can be distributedly estimated using pilot-assisted channel training [2]. Under TDD mode, assuming appropriately calibrated RF chains, the corresponding local CSI at the transmitters can also be obtained distributedly by pilot-assisted channel training in the uplink [2].…”

“…The local CSI in the downlink can be distributedly estimated using pilot-assisted channel training [2]. Under TDD mode, assuming appropriately calibrated RF chains, the corresponding local CSI at the transmitters can also be obtained distributedly by pilot-assisted channel training in the uplink [2]. Given such a training setup, the coordinated precoding optimization algorithms can distributedly perform one optimization iteration per uplink/downlink training phase; this is our definition of an OTA iteration.…”

“…The matrix inverses of the Taylor linearization points can then be introduced as optimization variables {Y i k } and {Z i k } (see e.g. [7] or [2]), giving a linearized and extended problem:…”

“…Its implementation typically requires a large amount of channel state information (CSI) at the nodes of the network. Lately, several works (see [2] and references therein) have studied local CSI acquisition at the transmitters by exploiting the reciprocity of the channel when time-division duplex (TDD) is used. In this mode of operation, each node in the network can perform one optimization iteration based on its current knowledge of local effective channels [2].…”

“…Lately, several works (see [2] and references therein) have studied local CSI acquisition at the transmitters by exploiting the reciprocity of the channel when time-division duplex (TDD) is used. In this mode of operation, each node in the network can perform one optimization iteration based on its current knowledge of local effective channels [2]. This enables iterative coordinated precoding algorithms (see [3] and references therein) to be implemented in a fully distributed manner.…”