| Summary: | 博士 === 國立交通大學 === 電信工程研究所 === 100 === Data detection or fusion based on multiple received copies containing the same information arises in many applications. We consider the scenario that each copy is transmitted from the same source through a different wireless link to the same destination node (DN). These links include single-hop, direct source-to-destination (SD) links and two-hop links that require an intermediate decode-and-forward (DF) node to relay the source signal. Detection or fusion under such a circumstance often need channel side information (CSI) about the link reliability. For example, maximum likelihood (ML) detection of binary modulated signals in a DF based cooperative communication network (CCN), information about the bit error rates (ERs) of the hidden source-relay (SR) links is needed. Similarly, optimal data fusion based on multiple sensor measurements requires that the ERs of various SR links be available at the fusion center.
To estimate multiple ERs blindly at the DN in a binary modulated network, we convert the estimation problem into one of solving a system of nonlinear equations. Each equation arises from the fact that the success matching probability (SMP) that a bit transmitted over two independent links connecting the same source and destination results in identical destination decisions is nonlinearly related to the ERs of the two associated links. However, the number of distinct link pairs must be larger than the number of ERs to be estimated so that the system is not an underdetermined one. Various degrees of channel side information (CSI) about the ERs of the SD and relay-destination (RD) links is called for to remove the ambiguity arising from the insufficient number of links in the network and from that due to the symmetric nature of a cascaded source-relay-destination (SRD) link's ER as a function of its component SR and RD links' ERs.
We propose novel Monte-Carlo-based estimators that overcome all these shortcomings and accelerate the convergence speed. Our proposals involve injecting noise into the samples received by the DN. The injected noise in the first solution, called the virtual link aided (VLA) estimator, help creating virtual SD and RD links to release all CSI requirements, resolve the symmetric ambiguity and provide estimates for ERs of all component links. Using multitude of VLs, we can enhance the VLA scheme's performance and reduce the number of RNs required. The role the injected noise plays in another solution, called the importance-sampling-inspired (ISI) estimator, is different: it is used to modify link output statistics to improve the VLA estimator's convergence rate.
The latter approach exhibits a stochastic resonance effect, i.e.,
its mean squared estimation error (MSEE) performance is enhanced by injecting proper noise, and there exists an optimal injected noise power level that achieves the maximum improvement. The stochastic resonance effects are analyzed, and numerical examples are provided to display our estimators' MSEE behaviors, as well as to show that the ER performance of the optimal detector using the proposed estimators is almost as good as that with perfect ER information.
For nonbinary modulation based networks, a relation between the SMP of a link pair and the associated symbol error rates does not exist, hence the nonlinear system based moments approach is not directly applicable. A nonlinear optimization approach which we call LJW blind estimator [34] had been proposed. Unfortunately it requires
prohibitively high computational complexity unless M and the relay
numbers are small. We propose a suboptimal detector based on bit-level representation and a corresponding blind estimator to estimate the error rate of sensor nodes. The complexity of our estimator is much lower than that of LJW as we are able to obtain a closed-form salutation instead of employing an iterative algorithm for solving a nonlinear optimization. To further improve the convergence rate, we propose a noise-enhanced estimator. Simulation results show that the proposed suboptimal detector using the proposed blind estimator render negligible performance loss with respect to that of the optimal detector. A stochastic resonance phenomenon is observed in the estimator's mean square estimation error performance.
|
|---|
