16 research outputs found
Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN
© 2002-2012 IEEE. This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a user-centric cloud radio access network, where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted l1-norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker conditions of the WPM problem. Moreover, a low-complexity algorithm based on Newton's method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side. Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC
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
Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network
The limited fronthaul capacity imposes a challenge on the uplink of
centralized radio access network (C-RAN). We propose to boost the fronthaul
capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by
globally optimizing the power sharing between channel estimation and data
transmission both for the user devices (UDs) and the remote radio units (RRUs).
Intuitively, allocating more power to the channel estimation will result in
more accurate channel estimates, which increases the achievable throughput.
However, increasing the power allocated to the pilot training will reduce the
power assigned to data transmission, which reduces the achievable throughput.
In order to optimize the powers allocated to the pilot training and to the data
transmission of both the UDs and the RRUs, we assign an individual power
sharing factor to each of them and derive an asymptotic closed-form expression
of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN
consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU)
links. We then exploit the C-RAN architecture's central computing and control
capability for jointly optimizing the UDs' power sharing factors and the RRUs'
power sharing factors aiming for maximizing the fronthaul capacity. Our
simulation results show that the fronthaul capacity is significantly boosted by
the proposed global optimization of the power allocation between channel
estimation and data transmission both for the UDs and for their host RRUs. As a
specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs
deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing
factors improves 33\% compared with the one attained without optimizing power
sharing factors
Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces
Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS’s power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the nonconvexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance
Multiuser Full-Duplex Two-Way Communications via Intelligent Reflecting Surface
Low-cost passive intelligent reflecting surfaces (IRSs) have recently been
envisioned as a revolutionary technology capable of reconfiguring the wireless
propagation environment through carefully tuning reflection elements. This
paper proposes deploying an IRS to cover the dead zone of cellular multiuser
full-duplex (FD) two-way communication links while suppressing user-side
self-interference (SI) and co-channel interference (CI). Based on information
exchanged by the base station (BS) and all users, this approach can potentially
double the spectral efficiency. To ensure network fairness, we jointly optimize
the precoding matrix of the BS and the reflection coefficients of the IRS to
maximize the weighted minimum rate (WMR) of all users, subject to maximum
transmit power and unit-modulus constraints. We reformulate this non-convex
problem and decouple it into two subproblems. Then the optimization variables
in the equivalent problem are alternately optimized by adopting the block
coordinate descent (BCD) algorithm. In order to further reduce the
computational complexity, we propose the minorization-maximization (MM)
algorithm for optimizing the precoding matrix and the reflection coefficient
vector by defining minorizing functions in the surrogate problems. Finally,
simulation results confirm the convergence and efficiency of our proposed
algorithm, and validate the advantages of introducing IRS to improve coverage
in blind areas.Comment: Accepted by IEEE Transactions on Signal Processin