Murat Kocaoglu

Minimum Energy Channel Codes for Nanoscale Wireless Communications

It is essential to develop energy-efficient communication techniques for nanoscale wireless communications. In this paper, a new modulation and a novel minimum energy coding scheme (MEC) are proposed to achieve energy efficiency in wireless nanosensor networks (WNSNs). Unlike existing studies, MEC maintains the desired code distance to provide reliability, while minimizing energy. It is analytically shown that, with MEC, codewords can be decoded perfectly for large code distances, if the source set cardinality is less than the inverse of the symbol error probability. Performance evaluations show that MEC outperforms popular codes such as Hamming, Reed-Solomon and Golay in the average codeword energy sense.

Minimum energy coding for wireless nanosensor networks

Wireless nanosensor networks (WNSNs), which are collections of nanosensors with communication units, can be used for sensing and data collection with extremely high resolution and low power consumption for various applications. In order to realize WNSNs, it is essential to develop energy-efficient communication techniques, since nanonodes are severely energy-constrained. In this paper, a novel minimum energy coding scheme (MEC) is proposed to achieve energy-efficiency in WNSNs. Unlike the existing minimum energy codes, MEC maintains the desired Hamming distance, while minimizing energy, in order to provide reliability. It is analytically shown that, with MEC, codewords can be decoded perfectly for large code distance, if source set cardinality, M is less than inverse of symbol error probability, 1/ps. Performance analysis shows that MEC outperforms popular codes such as Hamming, Reed-Solomon and Golay in average energy per codeword sense.

Fundamentals of Green Communications and Computing: Modeling and Simulation

A layered architecture incorporates the concept of minimum energy consumption for communication links and computer networks with multiple terminals, where emission-reduction approaches based on information theory are impractical.

Communication theoretic analysis of the synaptic channel for cortical neurons

Abstract In this paper, we develop a realistic model of the synaptic multiple-input single-output (MISO) communication channel for cortical neurons. The synaptic channel weights change adaptively according to the rules of spike timing-dependent plasticity (STDP) to enable learning and memory within neuronal connections. We calculate the ergodic capacity of the synaptic multiple-input multiple-output (MIMO) communication channel, and investigate its performance using the statistical properties of neuro-spike communication. Moreover, we analyze the communication performance of synaptic channels in terms of decoding error probability, and define a lower bound on the synaptic multiple-input single-output (MISO) communication channel.

On the node density limits and rate-delay-energy tradeoffs in ad hoc nanonetworks with minimum energy coding

Ad-hoc nanonetworks are collections of nanonodes without central controller units, and are the most promising network architectures in nano communications. Derivation of maximum nanonode density can pave the way for determining the capacity of ad-hoc nanonetworks. We consider ad-hoc nanonetworks with minimum energy coding (MEC). Maximum nanonode density for reliable communication in an ad-hoc nanonetwork without any medium access control is derived, and density dependent reliability analysis is conducted. Rate-delay-energy tradeoffs are also investigated with achievable rates, with constant codebook size and constant Hamming distance, separately.

Diversity in diffusion-based molecular communication channel with drift

We utilize the well known Additive Inverse Gaussian Noise (AIGN) communication channel to investigate the effect of diversity in diffusion-based molecular communication with drift, where the transmitter releases different types of molecules to the fluid medium by encoding the information onto the release time and type of molecules. The fluid channel imposes extra delay on the communication, and the receiver decodes the encoded information by solely utilizing the molecular arrival times. In this paper, simple receiver models based on maximum likelihood estimation (MLE) are investigated. Furthermore, upper and lower bounds on the capacity of AIGN communication channel with molecular diversity are derived.

Stochastic Resonance in Graphene Bilayer Optical Nanoreceivers

Graphene, a 2-D sheet of carbon atoms, is believed to have diverse application areas ranging from medicine to communications. A novel application is using graphene as a photodetector in optical communications due to its superior optical and electrical properties such as wide and tunable absorption frequency range and high electron mobility. Noise, which is especially significant in nanoscale communications, is mostly seen as an adversary. Stochastic resonance (SR) is the performance enhancement of a system due to incorporation of noise. It is shown that the excess noise in nanocommunications can be used to improve the performance of a graphene bilayer photodetector system with hard threshold decoder, when received signals are subthreshold. SR arises due to the nonlinear nature of the hard decoder. First, the SR effect due to the background ambient noise and intentional light noise is analyzed. An approximate inverse signal-to-noise ratio expression is derived, which maximizes the mutual information. The effect of frequency on the mutual information is also investigated, and it is shown that the higher frequencies are more preferable for noise limited regimes. Later, the case with the intentional noise added to the top gate is investigated. It is shown that significant mutual information improvements are achieved for subthreshold signals, due to the multiplicative stochastic terms arising from the nonlinear graphene bilayer characteristics, i.e., the exponential dependence of photocurrent on the gate voltages. All the analytical results are verified with extensive simulations.

Energy Minimization With Network Coding via Latin Hypercubes

Network coding is mostly used to achieve the capacity of communication networks. In this paper, motivated by the nanoscale communications where the energy cost for the channel symbols is asymmetric due to the widely employed on–off keying modulation, we design energy-minimizing network codes. We develop the best mapping between the input and output symbols at the network coding node that minimizes the average codeword energy using Latin squares, which we call the minimum energy network code (MENC). We define the class of networks composed of coding nodes with N incoming and 1 outgoing symbols as in-N networks. First, we derive the condition on the network code to minimize the average energy in in-two networks and propose two linear MENCs. Later, we investigate the MENCs for in-N networks using the Latin hypercubes and propose a low-energy network code (LENC) to reduce the average energy with network coding. We compare MENC with the classical XOR and random network codes for in-two networks. The performance comparison between LENC and random network codes for in-N networks shows that the proposed network codes provide significant energy gains.

Effect of channel conditions on inventory database update in supply chains

The supply chain optimization is becoming more and more important, and more challenging due to its increased complexity. Companies should properly exploit ICT systems to better keep track of their products. Communication techniques employed in the supply chain should be adaptive to any channel condition to assure continuous reliable operation. In this paper, we investigate the effects of channel conditions and the selected error control technique on the supply chain inventory database update in terms of database error rate and end-to-end delay. We derive the analytical expressions and verify them with extensive simulations and show that channel codes adapting to channel conditions would improve reliability of the inventory database.