Adaptive Randomized Distributed Space-Time Coding in Cooperative MIMO Relay Systems

An adaptive randomized distributed space-time coding (DSTC) scheme and algorithms are proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receivers and an amplify-and-forward (AF) cooperation strategy are considered. In the proposed DSTC scheme, a randomized matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. Linear MMSE expressions are devised to compute the parameters of the adaptive randomized matrix and the linear receive filter. A stochastic gradient algorithm is also developed to compute the parameters of the adaptive randomized matrix with reduced computational complexity. We also derive the upper bound of the error probability of a cooperative MIMO system employing the randomized space-time coding scheme first. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.Comment: 4 figure

Cooperative Symbol-Based Signaling for Networks with Multiple Relays

Wireless channels suffer from severe inherent impairments and hence reliable and high data rate wireless transmission is particularly challenging to achieve. Fortunately, using multiple antennae improves performance in wireless transmission by providing space diversity, spatial multiplexing, and power gains. However, in wireless ad-hoc networks multiple antennae may not be acceptable due to limitations in size, cost, and hardware complexity. As a result, cooperative relaying strategies have attracted considerable attention because of their abilities to take advantage of multi-antenna by using multiple single-antenna relays. This study is to explore cooperative signaling for different relay networks, such as multi-hop relay networks formed by multiple single-antenna relays and multi-stage relay networks formed by multiple relaying stages with each stage holding several single-antenna relays. The main contribution of this study is the development of a new relaying scheme for networks using symbol-level modulation, such as binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK). We also analyze effects of this newly developed scheme when it is used with space-time coding in a multi-stage relay network. Simulation results demonstrate that the new scheme outperforms previously proposed schemes: amplify-and-forward (AF) scheme and decode-and-forward (DF) scheme

Piggybacking Codes for Network Coding: The High/Low SNR Regime

We propose a piggybacking scheme for network coding where strong source inputs piggyback the weaker ones, a scheme necessary and sufficient to achieve the cut-set upper bound at high/low-snr regime, a new asymptotically optimal operational regime for the multihop Amplify and Forward (AF) networks

Optimal Training Design for Channel Estimation in Decode-and-Forward Relay Networks With Individual and Total Power Constraints

In this paper, we study the channel estimation and the optimal training design for relay networks that operate under the decode-and-forward (DF) strategy with the knowledge of the interference covariance. In addition to the total power constraint on all the relays, we introduce individual power constraint for each relay, which reflects the practical scenario where all relays are separated from one another. Considering the individual power constraint for the relay networks is the major difference from that in the traditional point-to-point communication systems where only a total power constraint exists for all colocated antennas. Two types of channel estimation are involved: maximum likelihood (ML) and minimum mean square error (MMSE). For ML channel estimation, the channels are assumed as deterministic and the optimal training results from an efficient multilevel waterfilling type solution that is derived from the majorization theory. For MMSE channel estimation, however, the second-order statistics of the channels are assumed known and the general optimization problem turns out to be nonconvex. We instead consider three special yet reasonable scenarios. The problem in the first scenario is convex and could be efficiently solved by state-of-the-art optimization tools. Closed-form waterfilling type solutions are found in the remaining two scenarios, of which the first one has an interesting physical interpretation as pouring water into caves