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

On channel estimation and optimal training design for amplify and forward relay networks

10.1109/GLOCOM.2007.763GLOBECOM - IEEE Global Telecommunications Conference4015-401

Massive MIMO Full-Duplex Relaying with Optimal Power Allocation for Independent Multipairs

With the help of an in-band full-duplex relay station, it is possible to simultaneously transmit and receive signals from multiple users. The performance of such system can be greatly increased when the relay station is equipped with a large number of antennas on both transmitter and receiver sides. In this paper, we exploit the use of massive arrays to effectively suppress the loopback interference (LI) of a decode-and-forward relay (DF) and evaluate the performance of the end-to-end (e2e) transmission. This paper assumes imperfect channel state information is available at the relay and designs a minimum mean-square error (MMSE) filter to mitigate the interference. Subsequently, we adopt zero-forcing (ZF) filters for both detection and beamforming. The performance of such system is evaluated in terms of bit error rate (BER) at both relay and destinations, and an optimal choice for the transmission power at the relay is shown. We then propose a complexity efficient optimal power allocation (OPA) algorithm that, using the channel statistics, computes the minimum power that satisfies the rate constraints of each pair. The results obtained via simulation show that when both MMSE filtering and OPA method are used, better values for the energy efficiency are attained.Comment: Accepted to the 16th IEEE International Workshop on Signal Processing Advances in Wireless Communications - SPAWC, Stockholm, Sweden 201

Maximum likelihood channel estimation in decode-and-forward relay networks

In this paper, we provide a complete study on the training based channel estimation for relay networks that employ the decode-and-forward (DF) scheme. Since multiple relay nodes are geographically distributed over the service region, channel estimation is different from the traditional way in that each relay has its own individual power constraint. We consider the maximum likelihood (ML) channel estimation and derive closed form solutions for the optimal training as well as for the optimal power allocation. It is seen that the optimal power allocation follows a multi-level waterfilling structure

Optimal Resource Allocation and Relay Selection in Bandwidth Exchange Based Cooperative Forwarding

In this paper, we investigate joint optimal relay selection and resource allocation under bandwidth exchange (BE) enabled incentivized cooperative forwarding in wireless networks. We consider an autonomous network where N nodes transmit data in the uplink to an access point (AP) / base station (BS). We consider the scenario where each node gets an initial amount (equal, optimal based on direct path or arbitrary) of bandwidth, and uses this bandwidth as a flexible incentive for two hop relaying. We focus on alpha-fair network utility maximization (NUM) and outage reduction in this environment. Our contribution is two-fold. First, we propose an incentivized forwarding based resource allocation algorithm which maximizes the global utility while preserving the initial utility of each cooperative node. Second, defining the link weight of each relay pair as the utility gain due to cooperation (over noncooperation), we show that the optimal relay selection in alpha-fair NUM reduces to the maximum weighted matching (MWM) problem in a non-bipartite graph. Numerical results show that the proposed algorithms provide 20- 25% gain in spectral efficiency and 90-98% reduction in outage probability.Comment: 8 pages, 7 figure