129 research outputs found
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
A Deep Learning Framework for Optimization of MISO Downlink Beamforming
Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems
Transmit Beamforming Design with Received-Interference Power Constraints: The Zero-Forcing Relaxation
The use of multi-antenna transmitters is emerging as an essential technology of the future wireless communication systems. While Zero-Forcing Beamforming (ZFB) has become the most popular low-complexity transmit beamforming design, it has some drawbacks basically related to the effort of "trying" to invert the channel coefficients towards the interfered users. In particular, ZFB performs poorly in the low Signal-to-Noise Ratio (SNR) regime and does not work when the interfered users outnumber the transmit antennas. In this paper, we study in detail an alternative transmit beamforming design framework, where we allow some residual received-interference power instead of trying to null it completely out. Subsequently, we provide a close-form non-iterative optimal solution that avoids the use of sophisticated convex optimization techniques that compromise its applicability onto practical systems. Supporting results based on numerical simulations show that the proposed transmit beamforming is able to perform close to the optimal with much lower computational complexity.Grant numbers : TERESA - Hybrid TERrEstrial/Satellite Air Interface for 5G and Beyond project (code : TEC2017-90093-C3-1-R).@ 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Multicell Coordinated Beamforming with Rate Outage Constraint--Part I: Complexity Analysis
This paper studies the coordinated beamforming (CoBF) design in the
multiple-input single-output interference channel, assuming only channel
distribution information given a priori at the transmitters. The CoBF design is
formulated as an optimization problem that maximizes a predefined system
utility, e.g., the weighted sum rate or the weighted max-min-fairness (MMF)
rate, subject to constraints on the individual probability of rate outage and
power budget. While the problem is non-convex and appears difficult to handle
due to the intricate outage probability constraints, so far it is still unknown
if this outage constrained problem is computationally tractable. To answer
this, we conduct computational complexity analysis of the outage constrained
CoBF problem. Specifically, we show that the outage constrained CoBF problem
with the weighted sum rate utility is intrinsically difficult, i.e., NP-hard.
Moreover, the outage constrained CoBF problem with the weighted MMF rate
utility is also NP-hard except the case when all the transmitters are equipped
with single antenna. The presented analysis results confirm that efficient
approximation methods are indispensable to the outage constrained CoBF problem.Comment: submitted to IEEE Transactions on Signal Processin
Fundamental Limits of Cooperation
Cooperation is viewed as a key ingredient for interference management in
wireless systems. This paper shows that cooperation has fundamental
limitations. The main result is that even full cooperation between transmitters
cannot in general change an interference-limited network to a noise-limited
network. The key idea is that there exists a spectral efficiency upper bound
that is independent of the transmit power. First, a spectral efficiency upper
bound is established for systems that rely on pilot-assisted channel
estimation; in this framework, cooperation is shown to be possible only within
clusters of limited size, which are subject to out-of-cluster interference
whose power scales with that of the in-cluster signals. Second, an upper bound
is also shown to exist when cooperation is through noncoherent communication;
thus, the spectral efficiency limitation is not a by-product of the reliance on
pilot-assisted channel estimation. Consequently, existing literature that
routinely assumes the high-power spectral efficiency scales with the log of the
transmit power provides only a partial characterization. The complete
characterization proposed in this paper subdivides the high-power regime into a
degrees-of-freedom regime, where the scaling with the log of the transmit power
holds approximately, and a saturation regime, where the spectral efficiency
hits a ceiling that is independent of the power. Using a cellular system as an
example, it is demonstrated that the spectral efficiency saturates at power
levels of operational relevance.Comment: 27 page
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