70 research outputs found
Robust SINR-Constrained Symbol-Level Multiuser Precoding With Imperfect Channel Knowledge
In this paper, we address robust design of symbol-level precoding (SLP) for the downlink of multiuser multiple-input single-output wireless channels, when imperfect channel state information (CSI) is available at the transmitter. In particular, we consider a well known model for the CSI imperfection, namely, stochastic Gaussian-distributed uncertainty. Our design objective is to minimize the total (per-symbol) transmission power subject to constructive interference (CI) constraints as well as the users’ quality-of-service requirements in terms of signal-to-interference-plus-noise ratio. Assuming stochastic channel uncertainties, we first define probabilistic CI constraints in order to achieve robustness to statistically known CSI errors. Since these probabilistic constraints are difficult to handle, we resort to their convex approximations in the form of tractable (deterministic) robust constraints. Three convex approximations are obtained based on different conservatism levels, among which one is introduced as a benchmark for comparison. We show that each of our proposed approximations is tighter than the other under specific robustness settings, while both of them always outperform the benchmark. Using the proposed CI constraints, we formulate the robust SLP optimization problem as a second-order cone program. Extensive simulation results are provided to validate our analytic discussions and to make comparisons with conventional block-level robust precoding schemes. We show that the robust design of symbol-level precoder leads to an improved performance in terms of energy efficiency at the cost of increasing the computational complexity by an order of the number of users in the large system limit, compared to its non-robust counterpart
Robust SINR-Constrained Symbol-Level Multiuser Precoding with Imperfect Channel Knowledge
In this paper, we address robust design of symbol-level precoding for the
downlink of multiuser multiple-input multiple-output wireless channels, in the
presence of imperfect channel state information (CSI) at the transmitter. In
particular, we consider two common uncertainty models for the CSI imperfection,
namely, spherical (bounded) and stochastic (Gaussian). Our design objective is
to minimize the total (per-symbol) transmission power subject to constructive
interference (CI) constraints as well as users' quality-of-service requirements
in terms of signal-to-interference-plus-noise ratio. Assuming bounded channel
uncertainties, we obtain a convex CI constraint based on the worst-case robust
analysis, whereas in the case of Gaussian uncertainties, we define
probabilistic CI constraints in order to achieve robustness to
statistically-known CSI errors. Since the probabilistic constraints of actual
interest are difficult to handle, we resort to their convex approximations,
yielding tractable (deterministic) robust constraints. Three convex
approximations are developed based on different robust conservatism approaches,
among which one is introduced as a benchmark for comparison. We show that each
of our proposed approximations is tighter than the other under specific
robustness conditions, while both always outperform the benchmark. Using the
developed CI constraints, we formulate the robust precoding optimization as a
convex conic quadratic program. Extensive simulation results are provided to
validate our analytic discussions and to make comparisons with existing robust
precoding schemes. We also show that the robust design increases the
computational complexity by an order of the number of users in the large system
limit, compared to its non-robust counterpart.Comment: 19 pages, 9 figures, Submitted to IEEE Transactions in Signal
Processin
Optimal and Robust Transmit Designs for MISO Channel Secrecy by Semidefinite Programming
In recent years there has been growing interest in study of multi-antenna
transmit designs for providing secure communication over the physical layer.
This paper considers the scenario of an intended multi-input single-output
channel overheard by multiple multi-antenna eavesdroppers. Specifically, we
address the transmit covariance optimization for secrecy-rate maximization
(SRM) of that scenario. The challenge of this problem is that it is a nonconvex
optimization problem. This paper shows that the SRM problem can actually be
solved in a convex and tractable fashion, by recasting the SRM problem as a
semidefinite program (SDP). The SRM problem we solve is under the premise of
perfect channel state information (CSI). This paper also deals with the
imperfect CSI case. We consider a worst-case robust SRM formulation under
spherical CSI uncertainties, and we develop an optimal solution to it, again
via SDP. Moreover, our analysis reveals that transmit beamforming is generally
the optimal transmit strategy for SRM of the considered scenario, for both the
perfect and imperfect CSI cases. Simulation results are provided to illustrate
the secrecy-rate performance gains of the proposed SDP solutions compared to
some suboptimal transmit designs.Comment: 32 pages, 5 figures; to appear, IEEE Transactions on Signal
Processing, 201
Tight Probabilistic SINR Constrained Beamforming Under Channel Uncertainties
In downlink multi-user beamforming, a single bases- tation is serving a number of users simultaneously. However, energy intended for one user may leak to other unintended users, causing interference. With signal-to-interference-plus-noise ratio (SINR) being one of the most crucial quality metrics to users, beamforming design with SINR guarantee has always been an important research topic. However, when the channel state information is not accurate, the SINR requirements become probabilistic constraints, which unfortunately are not tractable analytically for general uncertainty distribution. Therefore, ex- isting probabilistic beamforming methods focus on the relatively simple Gaussian and uniform channel uncertainties, and mainly rely on probability inequality based approximated solutions, resulting in conservative SINR outage realizations. In this paper, based on the local structure of the feasible set in the probabilistic beamforming problem, a systematic method is proposed to realize tight SINR outage control for a large class of channel uncertainty distributions. With channel estimation and quantization errors as examples, simulation results show that the SINR outage can be re- alized tightly, which results in reduced transmit power compared to the existing inequality based probabilistic beamformers.published_or_final_versio
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
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