8,224 research outputs found
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization
In this paper, an reconfigurable intelligent surface (RIS)-aided millimeter
wave (mmWave) non-orthogonal multiple access (NOMA) system is considered. In
particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid
beamforming structure. To maximize the achievable sum-rate under a minimum rate
constraint for the users and a minimum transmit power constraint, a joint RIS
phase shifts, hybrid beamforming, and power allocation problem is formulated.
To solve this non-convex optimization problem, we develop an alternating
optimization algorithm. Specifically, first, the non-convex problem is
transformed into three subproblems, i.e., power allocation, joint phase shifts
and analog beamforming optimization, and digital beamforming design. Then, we
solve the power allocation problem under fixed phase shifts of the RIS and
hybrid beamforming. Finally, given the power allocation matrix, an alternating
manifold optimization (AMO)-based method and a successive convex approximation
(SCA)-based method are utilized to design the phase shifts, analog beamforming,
and transmit beamforming, respectively. Numerical results reveal that the
proposed alternating optimization algorithm outperforms state-of-the-art
schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA
system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of
improving the achievable sum-rate of the system
Robust Adaptive Beamforming for General-Rank Signal Model with Positive Semi-Definite Constraint via POTDC
The robust adaptive beamforming (RAB) problem for general-rank signal model
with an additional positive semi-definite constraint is considered. Using the
principle of the worst-case performance optimization, such RAB problem leads to
a difference-of-convex functions (DC) optimization problem. The existing
approaches for solving the resulted non-convex DC problem are based on
approximations and find only suboptimal solutions. Here we solve the non-convex
DC problem rigorously and give arguments suggesting that the solution is
globally optimal. Particularly, we rewrite the problem as the minimization of a
one-dimensional optimal value function whose corresponding optimization problem
is non-convex. Then, the optimal value function is replaced with another
equivalent one, for which the corresponding optimization problem is convex. The
new one-dimensional optimal value function is minimized iteratively via
polynomial time DC (POTDC) algorithm.We show that our solution satisfies the
Karush-Kuhn-Tucker (KKT) optimality conditions and there is a strong evidence
that such solution is also globally optimal. Towards this conclusion, we
conjecture that the new optimal value function is a convex function. The new
RAB method shows superior performance compared to the other state-of-the-art
general-rank RAB methods.Comment: 29 pages, 7 figures, 2 tables, Submitted to IEEE Trans. Signal
Processing on August 201
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