258 research outputs found
Chance Constrained Robust Downlink Beamforming in Multicell Networks
We introduce a downlink robust optimization approach that minimizes a combination of total transmit power by a multiple antenna base station (BS) within a cell and the resulting aggregate inter-cell interference (ICI) power on the users of the other cells. This optimization is constrained to assure that a set of signal-to-interference-plus-noise ratio (SINR) targets are met at user terminals with certain outage probabilities. The outages are due to the uncertainties that naturally emerge in the estimation of channel covariance matrices between a BS and its intra-cell local users as well as the other users of the other cells. We model these uncertainties using random matrices, analyze their statistical behaviour and formulate a tractable probabilistic approach to the design of optimal robust downlink beamforming vectors. The proposed approach reformulates the original intractable non-convex problem in a semidefinite programming (SDP) form with linear matrix inequality (LMI) constraints. The resulting SDP formulation is convex and numerically tractable under the standard rank relaxation. We compare the proposed chance-constrained approach against two different robust design schemes as well as the worst-case robustness. The simulation results confirm better power efficiency and higher resilience against channel uncertainties of the proposed approach in realistic scenarios
Chance constrained robust downlink beamforming in multicell networks
We introduce a downlink robust optimization approach that minimizes a combination of total transmit power by a multiple antenna base station (BS) within a cell and the resulting aggregate inter-cell interference (ICI) power on the users of the other cells. This optimization is constrained to assure that a set of signal-to-interference-plus-noise ratio (SINR) targets are met at user terminals with certain outage probabilities. The outages are due to the uncertainties that naturally emerge in the estimation of channel covariance matrices between a BS and its intra-cell local users as well as the other users of the other cells. We model these uncertainties using random matrices, analyze their statistical behaviour and formulate a tractable probabilistic approach to the design of optimal robust downlink beamforming vectors. The proposed approach reformulates the original intractable non-convex problem in a semidefinite programming (SDP) form with linear matrix inequality (LMI) constraints. The resulting SDP formulation is convex and numerically tractable under the standard rank relaxation. We compare the proposed chance-constrained approach against two different robust design schemes as well as the worst-case robustness. The simulation results confirm better power efficiency and higher resilience against channel uncertainties of the proposed approach in realistic scenarios
Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems
We propose a subspace constrained precoding scheme that exploits the spatial
channel correlation structure in massive MIMO cellular systems to fully unleash
the tremendous gain provided by massive antenna array with reduced channel
state information (CSI) signaling overhead. The MIMO precoder at each base
station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace
control matrix. The inner precoder is adaptive to the local CSI at each BS for
spatial multiplexing gain. The Tx subspace control is adaptive to the channel
statistics for inter-cell interference mitigation and Quality of Service (QoS)
optimization. Specifically, the Tx subspace control is formulated as a QoS
optimization problem which involves an SINR chance constraint where the
probability of each user's SINR not satisfying a service requirement must not
exceed a given outage probability. Such chance constraint cannot be handled by
the existing methods due to the two stage precoding structure. To tackle this,
we propose a bi-convex approximation approach, which consists of three key
ingredients: random matrix theory, chance constrained optimization and
semidefinite relaxation. Then we propose an efficient algorithm to find the
optimal solution of the resulting bi-convex approximation problem. Simulations
show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication
Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks
In this paper, we develop various beamforming techniques for downlink
transmission for multiple-input single-output (MISO) non-orthogonal multiple
access (NOMA) systems. First, a beamforming approach with perfect channel state
information (CSI) is investigated to provide the required quality of service
(QoS) for all users. Taylor series approximation and semidefinite relaxation
(SDR) techniques are employed to reformulate the original non-convex power
minimization problem to a tractable one. Further, a fairness-based beamforming
approach is proposed through a max-min formulation to maintain fairness between
users. Next, we consider a robust scheme by incorporating channel
uncertainties, where the transmit power is minimized while satisfying the
outage probability requirement at each user. Through exploiting the SDR
approach, the original non-convex problem is reformulated in a linear matrix
inequality (LMI) form to obtain the optimal solution. Numerical results
demonstrate that the robust scheme can achieve better performance compared to
the non-robust scheme in terms of the rate satisfaction ratio. Further,
simulation results confirm that NOMA consumes a little over half transmit power
needed by OMA for the same data rate requirements. Hence, NOMA has the
potential to significantly improve the system performance in terms of transmit
power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog
Multi-Cell Interference Exploitation: Enhancing the Power Efficiency in Cell Coordination
In this paper, we propose a series of novel coordination schemes for multi-cell downlink communication. Starting
from full base station (BS) coordination, we first propose a
fully-coordinated scheme to exploit beneficial effects of both
inter-cell and intra-cell interference, based on sharing both
channel state information (CSI) and data among the BSs. To
reduce the coordination overhead, we then propose a partiallycoordinated scheme where only intra-cell interference is designed
to be constructive while inter-cell is jointly suppressed by the
coordinated BSs. Accordingly, the coordination only involves
CSI exchange and the need for sharing data is eliminated.
To further reduce the coordination overhead, a third scheme
is proposed, which only requires the knowledge of statistical
inter-cell channels, at the cost of a slight increase on the
transmission power. For all the proposed schemes, imperfect
CSI is considered. We minimize the total transmission power in
terms of probabilistic and deterministic optimizations. Explicitly,
the former statistically satisfies the users’ signal-to-interferenceplus-noise ratio (SINR) while the latter guarantees the SINR
requirements in the worst case CSI uncertainties. Simulation
verifies that our schemes consume much lower power compared
to the existing benchmarks, i.e., coordinated multi-point (CoMP)
and coordinated-beamforming (CBF) systems, opening a new
dimension on multi-cell coordination
Distributed Robust Multi-Cell Coordinated Beamforming with Imperfect CSI: An ADMM Approach
Multi-cell coordinated beamforming (MCBF), where multiple base stations (BSs)
collaborate with each other in the beamforming design for mitigating the
inter-cell interference, has been a subject drawing great attention recently.
Most MCBF designs assume perfect channel state information (CSI) of mobile
stations (MSs); however CSI errors are inevitable at the BSs in practice.
Assuming elliptically bounded CSI errors, this paper studies the robust MCBF
design problem that minimizes the weighted sum power of BSs subject to
worst-case signal-to-interference-plus-noise ratio (SINR) constraints on the
MSs. Our goal is to devise a distributed optimization method that can obtain
the worst-case robust beamforming solutions in a decentralized fashion, with
only local CSI used at each BS and little backhaul signaling for message
exchange between BSs. However, the considered problem is difficult to handle
even in the centralized form. We first propose an efficient approximation
method in the centralized form, based on the semidefinite relaxation (SDR)
technique. To obtain the robust beamforming solution in a decentralized
fashion, we further propose a distributed robust MCBF algorithm, using a
distributed convex optimization technique known as alternating direction method
of multipliers (ADMM). We analytically show the convergence of the proposed
distributed robust MCBF algorithm to the optimal centralized solution and its
better bandwidth efficiency in backhaul signaling over the existing dual
decomposition based algorithms. Simulation results are presented to examine the
effectiveness of the proposed SDR method and the distributed robust MCBF
algorithm
Robust Interference Exploitation for Multi-Cell Transmission
In this paper, we investigate power-efficient constructive interference (CI) exploitation in multi-cell coordination systems. By only sharing channel state information (CSI) among the coordinated base stations (BS)s, we propose a CI-based coordinated beamforming (CBF) scheme to judiciously exploit multiuser interference as a beneficial element rather than strictly mitigating it, while simultaneously suppressing inter-cell interference as a destructive element. Then taking imperfect channel state information (CSI) into consideration, we minimize the total transmission power consumption with multiple users’ probabilistic signal-to-interference-and-noise ratio (SINR) requirements, where the users’ SINR requirements are guaranteed in a statistical manner. Finally, under the presence of CSI error, simulation results demonstrate that the proposed CI-based CBF scheme consumes much lower transmission power compared to the classical CBF benchmarks, where both intra-cell multi-user and inter-cell interference need to be strictly cancelled as destructive elements. Last but not least, the incurred overhead and computational complexity of the proposed scheme are analytically analyzed, confirming its practicality as a new dimension on multi-cell coordination
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