629 research outputs found
Max-min transmit beamforming via iterative regularization
This paper introduces an iterative optimization framework to tackle the multi-group multicast Max-Min transmit beamforming problem. In each iteration, the optimization problem is decomposed into four sub-problems, all of which can be solved using computationally efficient algorithms. The advantage of proposed method lies in its ability to handle different types of signal constraints like total power and unimodularity; a feature not exhibited by other techniques. The proposed technique outperforms the well-known semidefinite relaxation method in terms of quality of solution
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
search
Linear Precoding Based on Polynomial Expansion: Large-Scale Multi-Cell MIMO Systems
Large-scale MIMO systems can yield a substantial improvement in spectral
efficiency for future communication systems. Due to the finer spatial
resolution achieved by a huge number of antennas at the base stations, these
systems have shown to be robust to inter-user interference and the use of
linear precoding is asymptotically optimal. However, most precoding schemes
exhibit high computational complexity as the system dimensions increase. For
example, the near-optimal RZF requires the inversion of a large matrix. This
motivated our companion paper, where we proposed to solve the issue in
single-cell multi-user systems by approximating the matrix inverse by a
truncated polynomial expansion (TPE), where the polynomial coefficients are
optimized to maximize the system performance. We have shown that the proposed
TPE precoding with a small number of coefficients reaches almost the
performance of RZF but never exceeds it. In a realistic multi-cell scenario
involving large-scale multi-user MIMO systems, the optimization of RZF
precoding has thus far not been feasible. This is mainly attributed to the high
complexity of the scenario and the non-linear impact of the necessary
regularizing parameters. On the other hand, the scalar weights in TPE precoding
give hope for possible throughput optimization. Following the same methodology
as in the companion paper, we exploit random matrix theory to derive a
deterministic expression for the asymptotic SINR for each user. We also provide
an optimization algorithm to approximate the weights that maximize the
network-wide weighted max-min fairness. The optimization weights can be used to
mimic the user throughput distribution of RZF precoding. Using simulations, we
compare the network throughput of the TPE precoding with that of the suboptimal
RZF scheme and show that our scheme can achieve higher throughput using a TPE
order of only 3
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