14 research outputs found
Statistical Eigenmode Transmission over Jointly-Correlated MIMO Channels
We investigate MIMO eigenmode transmission using statistical channel state
information at the transmitter. We consider a general jointly-correlated MIMO
channel model, which does not require separable spatial correlations at the
transmitter and receiver. For this model, we first derive a closed-form tight
upper bound for the ergodic capacity, which reveals a simple and interesting
relationship in terms of the matrix permanent of the eigenmode channel coupling
matrix and embraces many existing results in the literature as special cases.
Based on this closed-form and tractable upper bound expression, we then employ
convex optimization techniques to develop low-complexity power allocation
solutions involving only the channel statistics. Necessary and sufficient
optimality conditions are derived, from which we develop an iterative
water-filling algorithm with guaranteed convergence. Simulations demonstrate
the tightness of the capacity upper bound and the near-optimal performance of
the proposed low-complexity transmitter optimization approach.Comment: 32 pages, 6 figures, to appear in IEEE Transactions on Information
Theor
Dynamic Metasurface Antennas for Energy Efficient Massive MIMO Uplink Communications
Future wireless communications are largely inclined to deploy a massive
number of antennas at the base stations (BS) by exploiting energy-efficient and
environmentally friendly technologies. An emerging technology called dynamic
metasurface antennas (DMAs) is promising to realize such massive antenna arrays
with reduced physical size, hardware cost, and power consumption. This paper
aims to optimize the energy efficiency (EE) performance of DMAs-assisted
massive MIMO uplink communications. We propose an algorithmic framework for
designing the transmit precoding of each multi-antenna user and the DMAs tuning
strategy at the BS to maximize the EE performance, considering the availability
of the instantaneous and statistical channel state information (CSI),
respectively. Specifically, the proposed framework includes Dinkelbach's
transform, alternating optimization, and deterministic equivalent methods. In
addition, we obtain a closed-form solution to the optimal transmit signal
directions for the statistical CSI case, which simplifies the corresponding
transmission design. The numerical results show good convergence performance of
our proposed algorithms as well as considerable EE performance gains of the
DMAs-assisted massive MIMO uplink communications over the baseline schemes