102 research outputs found
Maximizing sum rate and minimizing MSE on multiuser downlink: Optimality, fast algorithms and equivalence via max-min SIR
Maximizing the minimum weighted SIR, minimizing the weighted sum MSE and maximizing the weighted sum rate in a multiuser downlink system are three important performance objectives in joint transceiver and power optimization, where all the users have a total power constraint. We show that, through connections with the nonlinear Perron-Frobenius theory, jointly optimizing power and beamformers in the max-min weighted SIR problem can be solved optimally in a distributed fashion. Then, connecting these three performance objectives through the arithmetic-geometric mean inequality and nonnegative matrix theory, we solve the weighted sum MSE minimization and weighted sum rate maximization in the low to moderate interference regimes using fast algorithms
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters
A multicell-coordinated beamforming solution for massive multiple-input
multiple-output orthogonal frequency-division multiplexing (OFDM) systems is
presented when employing low-resolution data converters and per-antenna level
constraints. For a more realistic deployment, we aim to find the downlink (DL)
beamformer that minimizes the maximum power on transmit antenna array of each
basestation under received signal quality constraints while minimizing
per-antenna transmit power. We show that strong duality holds between the
primal DL formulation and its manageable Lagrangian dual problem which can be
interpreted as the virtual uplink (UL) problem with adjustable noise covariance
matrices. For a fixed set of noise covariance matrices, we claim that the
virtual UL solution is effectively used to compute the DL beamformer and noise
covariance matrices can be subsequently updated with an associated subgradient.
Our primary contributions are then (1) formulating the quantized DL OFDM
antenna power minimax problem and deriving its associated dual problem, (2)
showing strong duality and interpreting the dual as a virtual quantized UL OFDM
problem, and (3) developing an iterative minimax algorithm based on the dual
problem. Simulations validate the proposed algorithm in terms of the maximum
antenna transmit power and peak-to-average-power ratio.Comment: submitted for possible IEEE journal publicatio
Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition
This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on
Wireless Communications are correcte
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