Cross Layer Optimization in Wireless Communications Network towards Autonomous Resource Allocation and Adaptive Coding
Cross layer optimization is one of the crucial issues that have to be solved when designing spectrum- and power-efficient ubiquitous wireless networks. This research category includes a lot of issues, all related cross layer optimization , such as optimal resource allocation, adaptive coding and modulation, and scheduling. The major aim of this research category is to create algorithms that can bring autonomously the wireless networks to the optimal operation points using the message passing algorithm over the network nodes.
Semantic Language Analysis using Turbo Algorithms
Language recognition systems can be seen as a system having distributed multiple local decision making nodes. By utilizing the message passing technique over the multiple decision making nodes at different understanding level, the language literacy, as a whole, is expected to be significantly enhanced, especially in the noisy environments. This research aims to establish algorithms that can solve the semantic level language analysis using the Turbo techniques.
Turbo Estimation Techniques for Channel Prediction, Filtering, and Synchronization.
This research aims to apply the Turbo techniques to solving several estimation problems in wireless communications, including channel estimation, prediction, and synchronization. The factor graph-based detection and estimation technique using message passing algorithm will be used.
Joint Decoding of Source and Channel Codes using Message Techniques
Joint decoding of source and channel codes using the Turbo principle is sought for. Convergence property analysis using extrinsic information transfer chart provides us with the information about the matching optimality of the codes, and hence the EXIT curve matching techniques will be used as a tool for the optimization.
Optimal Activation Control of Multiple Turbo Loops
To detect signals via detector-decoder chains having multiple Turbo loops, the optimal path in the extrinsic information transfer plain has to be found to minimize the decoding/detection complexity. The primary goal of this research is to develop algorithms that can achieve the optimality in activation control of the multiple Turbo loops to minimize the decoding/detection complexity.
A Unified Approach to the MAC and Slepian-Wolf Region and its Applications
The primary goal of this research is to establish methodologies allowing us to calculate the multiple access (MAC) and Slepian Wolf regions for correlated sources. Major applications of the outcomes of this research include joint optimization of cooperative source and channel coding in sensor and/or relaying networks.
Compression Techniques for Sensor Network
The purpose of this research work is to fulfill the battery longevity requirement in sensor network by significantly reducing the information bit rate of the signal transmitted from sensors. To achieve this goal, Turbo decoding techniques will be used, where the correlation between the multiple sources is modeled as a hidden Markov source, and message passing takes place over the trellis diagrams representing the source correlation.
Cooperative Coding for Multi-Hop Networks
In wireless multi-hop networks, cooperative coding techniques allow us to achieve diversity and coding gains, while also improving the throughput efficiency. This research work aims to develop signal relaying algorithms where account is taken of the fact that the signals received from the primary sender's and relayed terminals are correlated; The correlation is first estimated by the receiver, and then decoding of the codes used for relaying is performed using Turbo techniques.