184 research outputs found
Sum-Rate Maximization in Two-Way AF MIMO Relaying: Polynomial Time Solutions to a Class of DC Programming Problems
Sum-rate maximization in two-way amplify-and-forward (AF) multiple-input
multiple-output (MIMO) relaying belongs to the class of difference-of-convex
functions (DC) programming problems. DC programming problems occur as well in
other signal processing applications and are typically solved using different
modifications of the branch-and-bound method. This method, however, does not
have any polynomial time complexity guarantees. In this paper, we show that a
class of DC programming problems, to which the sum-rate maximization in two-way
MIMO relaying belongs, can be solved very efficiently in polynomial time, and
develop two algorithms. The objective function of the problem is represented as
a product of quadratic ratios and parameterized so that its convex part (versus
the concave part) contains only one (or two) optimization variables. One of the
algorithms is called POlynomial-Time DC (POTDC) and is based on semi-definite
programming (SDP) relaxation, linearization, and an iterative search over a
single parameter. The other algorithm is called RAte-maximization via
Generalized EigenvectorS (RAGES) and is based on the generalized eigenvectors
method and an iterative search over two (or one, in its approximate version)
optimization variables. We also derive an upper-bound for the optimal values of
the corresponding optimization problem and show by simulations that this
upper-bound can be achieved by both algorithms. The proposed methods for
maximizing the sum-rate in the two-way AF MIMO relaying system are shown to be
superior to other state-of-the-art algorithms.Comment: 35 pages, 10 figures, Submitted to the IEEE Trans. Signal Processing
in Nov. 201
Robust Beamforming for Two-Way Relay Systems
In wireless communication systems, relays are widely used to extend coverage. Over the past years, relays have evolved from simple repeaters to more sophisticated units that perform signal processing to improve signal to interference plus noise ratio (SINR) or throughput (or both) at the destination receiver. There are various types of relays such as amplify and forward (AF), decode and forward (DF), and compress and forward (CF) (or estimate and forward (EF)) relays. In addition, recently there has been a growing interest in two-way relays (TWR). By utilizing the concept of analog network coding (ANC), TWRs can improve the throughput of a wireless sys- tem by reducing the number of time slots needed to complete a bi-directional message exchange between two destination nodes. It’s well known that the performance of a TWR system greatly depends on its ability to apply signal processing techniques to effectively mitigate the self-interference and noise accumulation, thereby improving the SINR. We study a TWR system that is equipped with multiple antennas at the relay node and a single antenna at the two destination nodes. Different from traditional work on TWR, we focus on the case with imperfect knowledge of channel state information (CSI).
For such a TWR, we formulate a robust optimization problem that takes into ac- count norm-bounded estimation errors in CSI and designs an optimal beamforming matrix. Realizing the fact that this problem is extremely hard to solve globally, we derive two different methods to obtain either optimal or efficient suboptimal beam- forming matrix solutions. The first method involves solving the robust optimization problem using the S-procedure and semidefinite programming (SDP) with rank-one relaxation. This method provides an optimal solution when the rank-one relaxation condition for the SDP is satisfied. In cases where the rank-one condition cannot be satisfied, it’s necessary to resort to sub-optimal techniques. The second approach presented here reformulates the robust non-convex quadratically constrained quadratic programming (QCQP) into a robust linear programming (LP) problem by using first-order perturbation of the optimal non-robust beamforming solution (which assumes no channel estimation error). Finally, we view the TWR robust beamforming problem from a practical standpoint and develop a set of iterative algorithms based on Newton’s method or the steepest descent method that are practical for hardware implementation
Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer
Wireless Power Transfer (WPT) is expected to be a technology reshaping the
landscape of low-power applications such as the Internet of Things, Radio
Frequency identification (RFID) networks, etc. Although there has been some
progress towards multi-antenna multi-sine WPT design, the large-scale design of
WPT, reminiscent of massive MIMO in communications, remains an open challenge.
In this paper, we derive efficient multiuser algorithms based on a
generalizable optimization framework, in order to design transmit sinewaves
that maximize the weighted-sum/minimum rectenna output DC voltage. The study
highlights the significant effect of the nonlinearity introduced by the
rectification process on the design of waveforms in multiuser systems.
Interestingly, in the single-user case, the optimal spatial domain beamforming,
obtained prior to the frequency domain power allocation optimization, turns out
to be Maximum Ratio Transmission (MRT). In contrast, in the general weighted
sum criterion maximization problem, the spatial domain beamforming optimization
and the frequency domain power allocation optimization are coupled. Assuming
channel hardening, low-complexity algorithms are proposed based on asymptotic
analysis, to maximize the two criteria. The structure of the asymptotically
optimal spatial domain precoder can be found prior to the optimization. The
performance of the proposed algorithms is evaluated. Numerical results confirm
the inefficiency of the linear model-based design for the single and multi-user
scenarios. It is also shown that as nonlinear model-based designs, the proposed
algorithms can benefit from an increasing number of sinewaves.Comment: Accepted to IEEE Transactions on Signal Processin
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