464 research outputs found
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
Robust Transceiver Design for MISO Interference Channel with Energy Harvesting
In this paper, we consider multiuser multiple-input single-output (MISO)
interference channel where the received signal is divided into two parts for
information decoding and energy harvesting (EH), respectively. The transmit
beamforming vectors and receive power splitting (PS) ratios are jointly
designed in order to minimize the total transmission power subject to both
signal-to-interference-plus-noise ratio (SINR) and EH constraints. Most joint
beamforming and power splitting (JBPS) designs assume that perfect channel
state information (CSI) is available; however CSI errors are inevitable in
practice. To overcome this limitation, we study the robust JBPS design problem
assuming a norm-bounded error (NBE) model for the CSI. Three different solution
approaches are proposed for the robust JBPS problem, each one leading to a
different computational algorithm. Firstly, an efficient semidefinite
relaxation (SDR)-based approach is presented to solve the highly non-convex
JBPS problem, where the latter can be formulated as a semidefinite programming
(SDP) problem. A rank-one recovery method is provided to recover a robust
feasible solution to the original problem. Secondly, based on second order cone
programming (SOCP) relaxation, we propose a low complexity approach with the
aid of a closed-form robust solution recovery method. Thirdly, a new iterative
method is also provided which can achieve near-optimal performance when the
SDR-based algorithm results in a higher-rank solution. We prove that this
iterative algorithm monotonically converges to a Karush-Kuhn-Tucker (KKT)
solution of the robust JBPS problem. Finally, simulation results are presented
to validate the robustness and efficiency of the proposed algorithms.Comment: 13 pages, 8 figures. arXiv admin note: text overlap with
arXiv:1407.0474 by other author
Robust Transmission in Downlink Multiuser MISO Systems: A Rate-Splitting Approach
We consider a downlink multiuser MISO system with bounded errors in the
Channel State Information at the Transmitter (CSIT). We first look at the
robust design problem of achieving max-min fairness amongst users (in the
worst-case sense). Contrary to the conventional approach adopted in literature,
we propose a rather unorthodox design based on a Rate-Splitting (RS) strategy.
Each user's message is split into two parts, a common part and a private part.
All common parts are packed into one super common message encoded using a
public codebook, while private parts are independently encoded. The resulting
symbol streams are linearly precoded and simultaneously transmitted, and each
receiver retrieves its intended message by decoding both the common stream and
its corresponding private stream. For CSIT uncertainty regions that scale with
SNR (e.g. by scaling the number of feedback bits), we prove that a RS-based
design achieves higher max-min (symmetric) Degrees of Freedom (DoF) compared to
conventional designs (NoRS). For the special case of non-scaling CSIT (e.g.
fixed number of feedback bits), and contrary to NoRS, RS can achieve a
non-saturating max-min rate. We propose a robust algorithm based on the
cutting-set method coupled with the Weighted Minimum Mean Square Error (WMMSE)
approach, and we demonstrate its performance gains over state-of-the art
designs. Finally, we extend the RS strategy to address the Quality of Service
(QoS) constrained power minimization problem, and we demonstrate significant
gains over NoRS-based designs.Comment: Accepted for publication in IEEE Transactions on Signal Processin
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
Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming
Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria
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