Communication between neurons occurs via transmission of neural spike trains through junctional structures, either electrical or chemical synapses, providing connections among nerve terminals. Since neural communication is achieved at synapses, the process of neurotransmission is called synaptic communication. Learning and memory processes are based on the changes in strength and connectivity of neural networks which usually contain multiple synaptic connections. In this paper, we investigate multiple-access neuro-spike communication channel, in which the neural signal, i.e., the action potential, is transmitted through multiple synaptic paths directed to a common postsynaptic neuron terminal. Synaptic transmission is initiated with random vesicle release process from presynaptic neurons to synaptic paths. Each synaptic channel is characterized by its impulse response and the number of available postsynaptic receptors. Here, we model the multiple-access synaptic communication channel, and investigate the information rate per spike at the postsynaptic neuron, and how postsynaptic rate is enhanced compared to single terminal synaptic communication channel. Furthermore, we analyze the synaptic transmission performance by incorporating the role of correlation among presynaptic terminals, and point out the postsynaptic rate improvement.

Device-to-device (D2D) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized opportunistic short-range communication. For D2D to be useful, mobile nodes must possess content that other mobiles want. Thus, intelligent caching techniques are essential for D2D. In this paper we use results from stochastic geometry to derive the probability of successful content delivery in the presence of interference and noise. We employ a general transmission strategy where multiple files are cached at the users and different files can be transmitted simultaneously throughout the network. We then formulate an optimization problem, and find the caching distribution that maximizes the density of successful receptions (DSR) under a simple transmission strategy where a single file is transmitted at a time throughout the network. We model file requests by a Zipf distribution with exponent γ r , which results in an optimal caching distribution that is also a Zipf distribution with exponent γ c , which is related to γ r through a simple expression involving the path loss exponent. We solve the optimal content placement problem for more general demand profiles under Rayleigh, Ricean and Nakagami small-scale fading distributions. Our results suggest that it is required to flatten the request distribution to optimize the caching performance. We also develop strategies to optimize content caching for the more general case with multiple files, and bound the DSR for that scenario.Parts of the manuscript were presented at the 2014 IEEE Globecom Workshops [1] and at the 2015 IEEE ICC Workshops [2].

Machine-to-machine (M2M) communication's severe power limitations challenge the interconnectivity, access management, and reliable communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the energy density of an M2M communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.2 Later in Sect. V, in evaluating the SIR-based coverage probability, we also incorporate the small-scale fading into the analysis that is assumed to be independent and identically distributed (iid) with unit mean. Therefore, incorporating fading yields the same average energy analysis. To keep the notation simple, we do not incorporate fading in Sects. II, III and IV.3 Na (random variable) denotes the number of devices in the Voronoi cell of a typical aggregator, and is detailed in Sect. III. 4 The interference is due to simultaneously active aggregator cells. Users within each Voronoi cell are assumed to use TDMA for access, and at a particular time slot, there is only one active transmitting device in each cell. For the sequential mode, the interference is due to the active transmitters outside the typical cell. On the other hand, for the parallel transmission mode, since all the stages are simultaneously active, there is both intra cell and out of cell interferences., which is detailed in Sect. VI. 5 In Sect. III, we will motivate the choice of γ < 0.5 in our setup.

We study optimal geographic content placement for device-to-device (D2D) networks in which each file's popularity follows the Zipf distribution. The locations of the D2D users (caches) are modeled by a Poisson point process (PPP) and have limited communication range and finite storage. Inspired by the Matérn hard-core (type II) point process that captures pairwise interactions between nodes, we devise a novel spatially correlated caching strategy called hard-core placement (HCP) such that the D2D nodes caching the same file are never closer to each other than the exclusion radius. The exclusion radius plays the role of a substitute for caching probability. We derive and optimize the exclusion radii to maximize the hit probability, which is the probability that a given D2D node can find a desired file at another node's cache within its communication range. Contrasting it with independent content placement, which is used in most prior work, our HCP strategy often yields a significantly higher cache hit probability. We further demonstrate that the HCP strategy is effective for small cache sizes and a small communication radius, which are likely conditions for D2D.

No abstract

No abstract

We propose a novel adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction (FEC) for a point-to-point communication channel with delayed feedback. AC-RLNC is adaptive to the channel condition, that the algorithm estimates, and is causal, as coding depends on the particular erasure realizations, as reflected in the feedback acknowledgments. Specifically, the proposed model can learn the erasure pattern of the channel via feedback acknowledgments, and adaptively adjust its retransmission rates using a priori and posteriori algorithms. By those adjustments, AC-RLNC achieves the desired delay and throughput, and enables transmission with zero error probability. We upper bound the throughput and the mean and maximum in order delivery delay of AC-RLNC, and prove that for the point to point communication channel in the non-asymptotic regime the proposed code may achieve more than 90% of the channel capacity. To upper bound the throughput we utilize the minimum Bhattacharyya distance for the AC-RLNC code. We validate those results via simulations. We contrast the performance of AC-RLNC with the one of selective repeat (SR)-ARQ, which is causal but not adaptive, and is a posteriori. Via a study on experimentally obtained commercial traces, we demonstrate that a protocol based on AC-RLNC can, vis-à-vis SR-ARQ, double the throughput gains, and triple the gain in terms of mean in order delivery delay when the channel is bursty. Furthermore, the difference between the maximum and mean in order delivery delay is much smaller than that of SR-ARQ. Closing the delay gap along with boosting the throughput is very promising for enabling ultra-reliable low-latency communications (URLLC) applications.Index Terms-Random linear network coding (RLNC), forward error correction (FEC), feedback, causal, coding, adaptive, in order delivery delay, throughput.

No abstract