On the Fundamental Feedback-vs-Performance Tradeoff over the MISO-BC with Imperfect and Delayed CSIT

This work considers the multiuser multiple-input single-output (MISO) broadcast channel (BC), where a transmitter with M antennas transmits information to K single-antenna users, and where - as expected - the quality and timeliness of channel state information at the transmitter (CSIT) is imperfect. Motivated by the fundamental question of how much feedback is necessary to achieve a certain performance, this work seeks to establish bounds on the tradeoff between degrees-of-freedom (DoF) performance and CSIT feedback quality. Specifically, this work provides a novel DoF region outer bound for the general K-user MISO BC with partial current CSIT, which naturally bridges the gap between the case of having no current CSIT (only delayed CSIT, or no CSIT) and the case with full CSIT. The work then characterizes the minimum CSIT feedback that is necessary for any point of the sum DoF, which is optimal for the case with M >= K, and the case with M=2, K=3.Comment: An initial version of this paper has been reported as Research Report No. RR-12-275 at EURECOM, December 7, 2012. This paper was submitted in part to the ISIT 201

Communication-optimal Parallel and Sequential Cholesky Decomposition

Numerical algorithms have two kinds of costs: arithmetic and communication, by which we mean either moving data between levels of a memory hierarchy (in the sequential case) or over a network connecting processors (in the parallel case). Communication costs often dominate arithmetic costs, so it is of interest to design algorithms minimizing communication. In this paper we first extend known lower bounds on the communication cost (both for bandwidth and for latency) of conventional (O(n^3)) matrix multiplication to Cholesky factorization, which is used for solving dense symmetric positive definite linear systems. Second, we compare the costs of various Cholesky decomposition implementations to these lower bounds and identify the algorithms and data structures that attain them. In the sequential case, we consider both the two-level and hierarchical memory models. Combined with prior results in [13, 14, 15], this gives a set of communication-optimal algorithms for O(n^3) implementations of the three basic factorizations of dense linear algebra: LU with pivoting, QR and Cholesky. But it goes beyond this prior work on sequential LU by optimizing communication for any number of levels of memory hierarchy.Comment: 29 pages, 2 tables, 6 figure