1. Information Sciences and Systems Lab
Introduction
Our research lies in the interface of communications, signal processing (computing) and networking. Sponsored by National Science Foundation, our recent work focuses on distributed and collaborative information processing and crosslayer design (with a physical layer emphasis) in wireless ad hoc and sensor networks, and associated information-theoretic and computation-theoretic analysis.
Energy-Efficient Distributed Detection
Virtual MIMO Communications in Wireless Networks
Distributed Detection Via Multihop Fusion
Significant energy reduction compared with direct transmission
Multihop forwarding
Histogram Fusion
Multihop Log-likelihood Ratio (LLR) Fusion
Joint optimization of fusion rules and transmission structure
LLR fusion performs best. S
Nodes in close proximity can cooperate in transmission to form a virtual MIMO system
Virtual MIMO reduces the energy consumption for the same throughput and BER target
Communications
- Relay/Cooperative diversity
- Virtual MIMO
- Distributed source coding
-- Information spreading …
Determination of the optimal transmission strategy depends on many interacting factors
Distributed Detection With A Multiple Access Channel
MAC fusion achieves centralized performance asymptotically with a properly designed local mapping rule.
Better bandwidth efficiency and detection performance than PAC (Parallel Access Channel) fusion
Signal Processing
- Detection and estimation
- Localization and tracking
- Statistical inference
- Distributed computation …
Networking
- Belief propagation
- Network coding
- Clustering
- Geographic routing …
Error rate for detection of a sinusoid signal in correlated Gaussian noise
Reach back problem
Receiver equipped with multiple antennas
Linear multiuser detector
Transmission is successful as long as received SIR is greater than a threshold (multi-packet reception)
Medium Access Schemes
Round-robin
Slotted ALOHA
Maximum Throughput Scheduling
Multi-antenna Receiver
Sensor Nodes
Accelerating Distributed Consensus
Optimal Throughput and Energy Efficiency for Sensor Networks: A Crosslayer Study
Dynamic Self-Calibration in Wireless Networks: A Belief Propagation Approach
We investigate distributed algorithms leading to faster computation of aggregate functions of node values
Cluster-based Solutions:
A cluster acts as an entity and information exchange among clusters
Faster convergence due to better connectivity
Less communication burden
Belief propagation is a general class of distributed message-passing algorithms, intended to solve the NP-hard probabilistic inference problems
Its application in wireless networks requires a reverse thinking, intended to serve as a general framework for collaborative information processing and dissemination
Particle filtering can be exploited for efficient message composition, processing and transmission in practice
For given MAC schemes and detectors, we optimize average number of transmissions per slot and the transmission power for
Throughput Maximization
Throughput-constrained Energy Minimization
For more information, contact:
Dr. Huaiyu Dai
2108 Engr Bldg II
Phone:
Huaiyu_Dai AT ncsu.edu
Nonreversible Random Walk Based Solutions
Known schemes are based on reversible chains
We propose a Location-aided Distributed Averaging (LADA) algorithm based on nonreversible chains
Node classifies neighbors by their relative locations
Converges substantially faster than known schemes