Ahmad Beirami

Capacity of discrete molecular diffusion channels

In diffusion-based molecular communications, messages can be conveyed via the variation in the concentration of molecules in the medium. In this paper, we intend to analyze the achievable capacity in transmission of information from one node to another in a diffusion channel. We observe that because of the molecular diffusion in the medium, the channel possesses memory. We then model the memory of the channel by a two-step Markov chain and obtain the equations describing the capacity of the diffusion channel. By performing a numerical analysis, we obtain the maximum achievable rate for different levels of the transmitter power, i.e., the molecule production rate.

Theoretical analysis of the characteristic impedance in metal–insulator–metal plasmonic transmission lines

We propose a closed form formulation for the impedance of the metal-insulator-metal (MIM) plasmonic transmission lines by solving the Maxwells equations. We provide approximations for thin and thick insulator layers sandwiched between metallic layers. In the case of very thin dielectric layer, the surface waves on both interfaces are strongly coupled resulting in an almost linear dependence of the impedance of the plasmonic transmission line on the thickness of the insulator layer. On the other hand, for very thick insulator layer, the impedance does not vary with the insulator layer thickness due to the weak-coupling/decoupling of the surface waves on each metal-insulator interface. We demonstrate the effectiveness of our proposed formulation using two test scenarios, namely, almost zero reflection in T-junction and reflection from line discontinuity in the design of Bragg reflectors, where we compare our formulation against previously published results.

Data gathering in networks of bacteria colonies: Collective sensing and relaying using molecular communication

The prospect of new biological and industrial applications that require communication in micro-scale, encourages research on the design of bio-compatible communication networks using networking primitives already available in nature. One of the most promising candidates for constructing such networks is to adapt and engineer specific types of bacteria that are capable of sensing, actuation, and above all, communication with each other. In this paper, we describe a new architecture for networks of bacteria to form a data collecting network, as in traditional sensor networks. The key to this architecture is the fact that the node in the network itself is a bacterial colony; as an individual bacterium (biological agent) is a tiny unreliable element with limited capabilities. We describe such a network under two different scenarios. We study the data gathering (sensing and multihop communication) scenario as in sensor networks followed by the consensus problem in a multinode network. We will explain as to how the bacteria in the colony collectively orchestrate their actions as a node to perform sensing and relaying tasks that would not be possible (at least reliably) by an individual bacterium. Each single bacterium in the colony forms a belief by sensing external parameter (e.g., a molecular signal from another node) from the medium and shares its belief with other bacteria in the colony. Then, after some interactions, all the bacteria in the colony form a common belief and act as a single node. We will model the reception process of each individual bacteria and will study its impact on the overall functionality of a node. We will present results on the reliability of the multihop communication for data gathering scenario as well as the speed of convergence in the consensus scenario.

Results on the fundamental gain of memory-assisted universal source coding

Many applications require data processing to be performed on individual pieces of data which are of finite sizes, e.g., files in cloud storage units and packets in data networks. However, traditional universal compression solutions would not perform well over the finite-length sequences. Recently, we proposed a framework called memory-assisted universal compression that holds a significant promise for reducing the amount of redundant data from the finite-length sequences. The proposed compression scheme is based on the observation that it is possible to learn source statistics (by memorizing previous sequences from the source) at some intermediate entities and then leverage the memorized context to reduce redundancy of the universal compression of finite-length sequences. We first present the fundamental gain of the proposed memory-assisted universal source coding over conventional universal compression (without memorization) for a single parametric source. Then, we extend and investigate the benefits of the memory-assisted universal source coding when the data sequences are generated by a compound source which is a mixture of parametric sources. We further develop a clustering technique within the memory-assisted compression framework to better utilize the memory by classifying the observed data sequences from a mixture of parametric sources. Finally, we demonstrate through computer simulations that the proposed joint memorization and clustering technique can achieve up to 6-fold improvement over the traditional universal compression technique when a mixture of non-binary Markov sources is considered.

Consensus problem under diffusion-based molecular communication

We investigate the consensus problem in a network where nodes communicate via diffusion-based molecular communication (DbMC). In DbMC, messages are conveyed via the variation in the concentration of molecules in the medium. Every node acquires sensory information about the environment. Communication enables the nodes to reach the best estimate for that measurement, e.g., the average of the initial estimates by all nodes. We consider an iterative method for communication among nodes that enables information spreading and averaging in the network. We show that the consensus can be attained after a finite number of iterations and variance of estimates of nodes can be made arbitrarily small via communication.

A realizable modified tent map for true random number generation

Tent map is a discrete-time piecewise-affine I/O characteristic curve, which is used for chaos-based applications, such as true random number generation. However, tent map suffers from the inability to maintain the output state confined to the input range under noise and process variations. In this paper, we propose a modified tent map, which is interchangeable with the tent map for practical applications. In the proposed modified tent map, the confinement problem is solved while maintaining the functionality of the tent map. We also demonstrate simulation results for the circuit implementation of the presented modified tent map for true random number generation.

A Framework for Investigating the Performance of Chaotic-Map Truly Random Number Generators

This brief approximates the hidden Markov model of chaotic-map truly random number generators (TRNGs) and describes its fundamental limits based on the approximate entropy rate of the underlying bit-generation process. It is demonstrated that the entropy rate plays a key role in the performance and robustness of chaotic-map TRNGs, which must be taken into account in the circuit design optimization. Finally, the optimality conditions for postprocessing units that extract truly random bits from a raw random number generator are derived.

Memory-assisted universal compression of network flows

Recently, the existence of considerable amount of redundancy in the Internet traffic has stimulated the deployment of several redundancy elimination techniques within the network. These techniques are often based on either packet-level Redundancy Elimination (RE) or Content-Centric Networking (CCN). However, these techniques cannot exploit sub-packet redundancies. Further, other alternative techniques such as the end-to-end universal compression solutions would not perform well either over the Internet traffic, as such techniques require infinite length traffic to effectively remove redundancy. This paper proposes a memory-assisted universal compression technique that holds a significant promise for reducing the amount of traffic in the networks. The proposed work is based on the observation that if a source is to be compressed and sent over a network, the associated universal code entails a substantial overhead in transmission due to finite length traffic. However, intermediate nodes can learn the source statistics and this can be used to reduce the cost of describing the source statistics, reducing the transmission overhead for such traffics. We present two algorithms (statistical and dictionary-based) for the memory-assisted universal lossless compression of information sources. These schemes are universal in the sense that they do not require any prior knowledge of the traffics statistical distribution. We demonstrate the effectiveness of both algorithms and characterize the memorization gain using the real Internet traces. Furthermore, we apply these compression schemes to Internet-like power-law graphs and solve the routing problem for compressed flows. We characterize the network-wide gain of the memorization from the information theoretic point of view. In particular, through our analysis on power-law graphs, we show that non-vanishing network-wide gain of memorization is obtained even when the number of memory units is a tiny fraction of the total number of nodes in the network. Finally, we validate our predictions of the memorization gain by simulation on real traffic traces.

On the network-wide gain of memory-assisted source coding

Several studies have identified a significant amount of redundancy in the network traffic. For example, it is demonstrated that there is a great amount of redundancy within the content of a server over time. This redundancy can be leveraged to reduce the network flow by the deployment of memory units in the network. The question that arises is whether or not the deployment of memory can result in a fundamental improvement in the performance of the network. In this paper, we answer this question affirmatively by first establishing the fundamental gains of memory-assisted source compression and then applying the technique to a network. Specifically, we investigate the gain of memory-assisted compression in random network graphs consisted of a single source and several randomly selected memory units. We find a threshold value for the number of memories deployed in a random graph and show that if the number of memories exceeds the threshold we observe network-wide reduction in the traffic.

On lossless universal compression of distributed identical sources

Slepian-Wolf theorem is a well-known framework that targets almost lossless compression of (two) data streams with symbol-by-symbol correlation between the outputs of (two) distributed sources. However, this paper considers a different scenario which does not fit in the Slepian-Wolf framework. We consider two identical but spatially separated sources. We wish to study the universal compression of a sequence of length n from one of the sources provided that the decoder has access to (i.e., memorized) a sequence of length m from the other source. Such a scenario occurs, for example, in the universal compression of data from multiple mirrors of the same server. In this setup, the correlation does not arise from symbol-by-symbol dependency of two outputs from the two sources. Instead, the sequences are correlated through the information that they contain about the unknown source parameter. We show that the finite-length nature of the compression problem at hand requires considering a notion of almost lossless source coding, where coding incurs an error probability pe(n) that vanishes with sequence length n. We obtain a lower bound on the average minimax redundancy of almost lossless codes as a function of the sequence length n and the permissible error probability pe when the decoder has a memory of length m and the encoders do not communicate. Our results demonstrate that a strict performance loss is incurred when the two encoders do not communicate even when the decoder knows the unknown parameter vector (i.e., m → ∞).