5 research outputs found
Optimal Compression and Transmission Rate Control for Node-Lifetime Maximization
We consider a system that is composed of an energy constrained sensor node
and a sink node, and devise optimal data compression and transmission policies
with an objective to prolong the lifetime of the sensor node. While applying
compression before transmission reduces the energy consumption of transmitting
the sensed data, blindly applying too much compression may even exceed the cost
of transmitting raw data, thereby losing its purpose. Hence, it is important to
investigate the trade-off between data compression and transmission energy
costs. In this paper, we study the joint optimal compression-transmission
design in three scenarios which differ in terms of the available channel
information at the sensor node, and cover a wide range of practical situations.
We formulate and solve joint optimization problems aiming to maximize the
lifetime of the sensor node whilst satisfying specific delay and bit error rate
(BER) constraints. Our results show that a jointly optimized
compression-transmission policy achieves significantly longer lifetime (90% to
2000%) as compared to optimizing transmission only without compression.
Importantly, this performance advantage is most profound when the delay
constraint is stringent, which demonstrates its suitability for low latency
communication in future wireless networks.Comment: accepted for publication in IEEE Transactions on Wireless
Communicaiton
A near-optimal LLR based cooperative spectrum sensing scheme for CRAHNs
In Cognitive Radio Ad Hoc Networks (CRAHNs), cooperative spectrum sensing schemes exploit spatial diversity of the Secondary Users (SUs), to reliably detect an unoccupied licensed spectrum. Soft energy combining schemes provide optimal detection performance by combining the actual sensed information from SUs. For reliable data fusion, these techniques mandate weight estimation for individual SUs in each sensing interval, resulting in high cooperation overhead in terms of time, processing and bandwidth. Alternately, a hard energy combining scheme offers lower cooperation overhead in which only local SU decisions are reported to the fusion center. However, it provides sub-optimal detection performance due to the information loss. In this paper, a Log-Likelihood Ratio (LLR) based cooperative spectrum sensing scheme is proposed in which each SU performs a local LLR based sensing test employing two threshold levels. The local decision and sequentially estimated SNR parameter values (for weight computation) are not reported to the fusion center if the local test result is in-between the two threshold levels. Thereby, cooperation overhead is reduced in proportion to the hard combining techniques; nevertheless simulation results show that the detection performance of the proposed scheme is close to the optimal soft combining techniques. 2002-2012 IEEE.Scopus2-s2.0-8493711733