2 research outputs found
Channel-Statistics-Based Hybrid Precoding for Millimeter-Wave MIMO Systems With Dynamic Subarrays
This paper investigates the hybrid precoding design for millimeter wave
(mmWave) multiple-input multiple-output (MIMO) systems with finite-alphabet
inputs. The mmWave MIMO system employs partially-connected hybrid precoding
architecture with dynamic subarrays, where each radio frequency (RF) chain is
connected to a dynamic subset of antennas. We consider the design of analog and
digital precoders utilizing statistical and/or mixed channel state information
(CSI), which involve solving an extremely difficult problem in theory: First,
designing the optimal partition of antennas over RF chains is a combinatorial
optimization problem, whose optimal solution requires an exhaustive search over
all antenna partitioning solutions; Second, the average mutual information
under mmWave MIMO channels lacks closed-form expression and involves
prohibitive computational burden; Third, the hybrid precoding problem with
given partition of antennas is nonconvex with respect to the analog and digital
precoders. To address these issues, this study first presents a simple
criterion and the corresponding low complexity algorithm to design the optimal
partition of antennas using statistical CSI. Then it derives the lower bound
and its approximation for the average mutual information, in which the
computational complexity is greatly reduced compared to calculating the average
mutual information directly. In addition, it also shows that the lower bound
with a constant shift offers a very accurate approximation to the average
mutual information. This paper further proposes utilizing the lower bound
approximation as a low-complexity and accurate alternative for developing a
manifold-based gradient ascent algorithm to find near optimal analog and
digital precoders. Several numerical results are provided to show that our
proposed algorithm outperforms existing hybrid precoding algorithms.Comment: Accepted by IEEE Transactions on Communication
Generalized Quadratic Matrix Programming: A Unified Framework for Linear Precoding With Arbitrary Input Distributions
This paper investigates a new class of non-convex optimization, which
provides a unified framework for linear precoding in single/multi-user
multiple-input multiple-output (MIMO) channels with arbitrary input
distributions. The new optimization is called generalized quadratic matrix
programming (GQMP). Due to the nondeterministic polynomial time (NP)-hardness
of GQMP problems, instead of seeking globally optimal solutions, we propose an
efficient algorithm which is guaranteed to converge to a Karush-Kuhn-Tucker
(KKT) point. The idea behind this algorithm is to construct explicit concave
lower bounds for non-convex objective and constraint functions, and then solve
a sequence of concave maximization problems until convergence. In terms of
application, we consider a downlink underlay secure cognitive radio (CR)
network, where each node has multiple antennas. We design linear precoders to
maximize the average secrecy (sum) rate with finite-alphabet inputs and
statistical channel state information (CSI) at the transmitter. The precoding
problems under secure multicast/broadcast scenarios are GQMP problems, and thus
they can be solved efficiently by our proposed algorithm. Several numerical
examples are provided to show the efficacy of our algorithm.Comment: TS