Efficient beamforming algorithms for MIMO multicast with application-layer coding

IEEE Trans. Wireless Commun.

2013

In this paper, we consider a two-hop multicasting multiple-input multiple-output (MIMO) relay system where one transmitter multicasts common message to multiple receivers with the aid of a relay node, and all nodes are equipped with multiple antennas. Joint transmit and relay precoding design problems are investigated for multicasting multiple data streams based on two design criteria. In the first scheme, we aim at minimizing the maximal mean-squared error (MSE) of the signal waveform estimation among all receivers subjecting to power constraints at the transmitter and the relay node. This problem is highly nonconvex with matrix variables and the exactly optimal solution is very hard to obtain. We develop an iterative algorithm to jointly optimize the transmitter, relay, and receiver matrices through solving convex subproblems. By exploiting the optimal structure of the relay precoding matrix, we then propose a low complexity solution which decouples the optimization of the transmitter and relay matrices under the (moderately) high first-hop signal-to-noise ratio (SNR) assumption. In the second scheme, we propose a total transmission power minimization strategy subjecting to quality-of-service (QoS) constraints. By using the optimal structure of the relay precoding matrix and the (moderately) high first-hop SNR assumption, we show that this problem can be solved using the semidefinite programming (SDP) technique. Numerical simulations demonstrate the effectiveness of the proposed algorithms. Interestingly, we show that for the special case of single data stream multicasting, the relay precoding matrix optimization problem can be equivalently converted to the transmit beamforming problem for single-hop multicasting systems.

Section: Introductionmentioning

confidence: 99%

Precoding Design for MIMO Relay Multicasting

Khandaker

IEEE Trans. Wireless Commun.

2013

“…In particular, coordinated beamforming techniques have been investigated in [15], where a generalized form of block diagonalization has been proposed to make orthogonal transmissions to distinct multicasting groups with multi-antenna receivers. The scaling of the achievable rate with increasing number of users was investigated in [16] for multiple-input multiple-output (MIMO) multicasting where the transmission is coded at the application layer over a number of channel realizations. In [17], non-iterative nearly optimal transmit beamformers are designed for wireless link layer multicasting with real-valued channels, and for complex-valued channels an upper-bound on the multicasting rate is derived.…”

Section: Introductionmentioning

confidence: 99%

Transceiver Optimization for Multi-Hop MIMO Relay Multicasting From Multiple Sources

Khandaker¹,

IEEE Trans. Wireless Commun.

2014

In this paper, we consider a multicasting multipleinput multiple-output (MIMO) relay system where multiple transmitters multicast their own messages to a group of receivers over multiple hops, and all nodes are equipped with multiple antennas. The joint transmit and relay precoding design problem has been investigated for multicasting multiple data streams based on min-max mean-squared error (MSE) criterion. We aim at minimizing the maximal MSE of the signal waveform estimation among all receivers subjecting to power constraints at the transmitters and all the relay nodes. This problem is highly nonconvex with matrix variables and the exactly optimal solution is very hard to obtain. We develop an iterative algorithm to jointly optimize the transmitter, relay, and receiver matrices by solving convex subproblems. By exploiting the optimal structure of the relay precoding matrices, we then propose a low complexity solution for the problem under some mild approximation. In particular, we show that under (moderately) high signal-to-noise ratio assumption, the min-max optimization problem can be solved using the semidefinite programming technique. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

“…The works in [4]- [5] solved the max-min signal-to-interference-plus-noise ratio (SINR)/rate beamforming problems with the aid of semidefinite relaxations (SDR). In [6], a stochastic beam forming strategy is proposed for multi-antenna physical-layer multicasting considering an achievable rate perspective where [7] focus on multicasting systems with single-antenna receiving nodes, recently multi-antenna receiving nodes have been considered in [8]- [10]. In particular, coordinated beamforming techniques have been investigated in [8] to facilitate physical layer multicasting with multi antenna receivers.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], non-iterative near-optimal transmit beamformers are designed for wireless link layer multicasting with real-valued channels, and for complex-valued channels an upper bound on the multicasting rate is derived. The scaling of the achievable rate for the increasing number of users has been investigated in [10] for multiple-input multiple-output (MIMO) multicasting in which the transmission is coded at the application layer over a number of channel realizations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multicasting MIMO relay optimization based on min-max MSE criterion

Khandaker

2012 IEEE International Conference on Communication Systems (ICCS)

2012

In this paper, we consider a multicasting multiple input multiple-output (MIMO) relay system where the transmit ter multi casts a common message to multiple receivers with the aid of a relay node, all equipped with multiple antennas. Given the power constraints at the source and the relay nodes, we aim at minimizing the maximal mean-squared error (MSE) of the signal waveform estimation among the destination nodes through joint source, relay, and receiver matrices optimization. We provide a low complexity solution to this highly nonconvex optimization problem. In particular, we show that under the (moderately) high signal-to-noise ratio (SNR) assumption, the joint source and relay optimization problem can be solved using standard semidefinite programming (SDP) technique. Numerical simulations provided demonstrate the effectiveness of the proposed algorithm.