Search

Various applications of molecular communications (MCs) feature an alarm-prompt behavior for which the prevalent Shannon capacity may not be the appropriate performance metric. The identification capacity as an alternative measure for such systems has been motivated and established in the literature. In this paper, we study deterministic K-identification (DKI) for the discrete-time Poisson channel (DTPC) with inter-symbol interference (ISI), where the transmitter is restricted to an average and a peak molecule release rate constraint. Such a channel serves as a model for diffusive MC systems featuring long channel impulse responses and employing molecule-counting receivers. We derive lower and upper bounds on the DKI capacity of the DTPC with ISI when the size of the target message set $K$ and the number of ISI channel taps $L$ may grow with the codeword length $n$ . As a key finding, we establish that for deterministic encoding, assuming that $K$ and $L$ both grow sub-linearly in $n$ , i.e., $K = 2^{\kappa \log n}$ and $L = 2^{l \log n} $ with $\kappa + 4l \in [0,1)$ , where $\kappa \in [0,1)$ is the identification target rate and $l \in [0,1/4) $ is the ISI rate, then the number of different messages that can be reliably identified scales super-exponentially in $n$ , i.e., $\sim 2^{(n\log n)R}$ , where $R$ is the DKI coding rate. Moreover, since $l$ and $\kappa $ must fulfill $\kappa + 4l \in [0,1)$ , we show that optimizing $l$ (or equivalently the symbol rate) leads to an effective identification rate [bits/s] that scales sub-linearly with $n $ . This result is in contrast to the typical transmission rate [bits/s] which is independent of $n$ .

Combining powerful error-correction codes (ECCs) with multipulse pulse-position modulation (MPPM) has great potential to realize high-throughput and high-reliability transmissions in free-space optical (FSO) communication systems. This paper conducts an insightful investigation on protograph low-density parity-check (PLDPC)-coded MPPM systems over Poisson channels. To be specific, we first put forward a novel type of constellations, referred to as irregular pulse-distribution (IPD) constellations, for MPPM. We also conceive a bit-level rate-adaptive (RA) scheme, which can exploit the structural property of the IPD constellations to significantly improve the convergence performance at the price of transmission-rate loss. Furthermore, with the aid of a modified protograph extrinsic information transfer (PEXIT) algorithm, we construct three enhanced PLDPC codes with code rates $1/2, 2/3$ and $3/4$ that exhibit excellent decoding thresholds over Poisson channels. The proposed PLDPC-coded MPPM systems with the use of the IPD constellations, RA scheme, and enhanced PLDPC codes are superior to the state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

In this work, we present a new machine learning-based framework for the accurate estimation of the shot-noise limited photon-counting Poisson channel by developing a state-of-art iterative unsupervised learning algorithm for intelligent optical communications. By accurate estimation, we mean that it achieves very low estimation error even when the effect of the received data is unpredictable. We consider a realistic situation where modulated symbols are assumed to be hidden or unobserved latent variables, thereby making conventional estimation algorithms based on maximum likelihood approach unsuitable or inefficient. In particular, we consider a probabilistic model and assume that the received data is not labeled. With this unpredictable data considered, a novel iterative machine learning framework is developed based on an expectation and maximization algorithm. The proposed algorithm avoids the need to choose an appropriate step size as required in gradient method based algorithms. It is shown that it significantly outperforms the least square and the Viterbi detection technique. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of joint transmit beamforming and power control for a multiuser multiple-input single-output (MISO) indoor optical downlink channel. We adopt a shot-noise limited Poisson channel with transmitting signals subject to average and peak transmits power constraints. To minimize the total transmit power, we formulate an optimal downlink beamforming problem using convex optimization method via second-order cone programming techniques. In our approach, the total transmit power is optimized subject to the signal-to-interference-plus-noise ratio (SINR). The results verify performance improvements of the proposed technique as compared to conventional multiuser MISO indoor optical systems.

Due to the strong scattering of ultraviolet (UV) communication, it is particularly suitable for scenarios involving short distance and large angle scattering, such as communication between buildings and jungles. In this paper, a system based on a single scattering model and a discrete Poisson channel pulse counting signal processing algorithm is constructed, and the effects of the number of signal photons, noise photons, and elevation angle on the system's bit error rate (BER) are simulated and analyzed. The effect of sampling rate on the system's performance is numerically analyzed. The results show that the pulse counting algorithm is suitable for systems with a small number of signal photons and a high sampling rate. The BER of the system when the number of signal photons becomes large is more dependent on the sampling rate than the signal-to-noise ratio. Additionally, we designed an outdoor test system including hardware system, pulse counting, synchronization and data recovery algorithms. The test results are consistent with the simulation results. The communication rate of the outdoor system is designed to be 10 kbps at a distance of 50 m. The BER is 10-3 when the elevation angle is 85°, while the BER is 10-5 when the elevation angle is 50°. • In this paper, a system based on a single scattering model and a discrete Poisson channel pulse counting signal processing algorithm is constructed, and the effects of the number of signal photons, the number of noise photons, and the elevation angle on the system's bit error rate (BER) are simulated and analyzed. The effect of sampling rate on the system performance is numerically analyzed. In addition, we designed an outdoor test system including hardware system, pulse counting, synchronization and data recovery algorithms. The test results are consistent with the simulation results. The BER is 10-3 when the elevation angle is 85° and 10-5 when the elevation angle is 50°. [ABSTRACT FROM AUTHOR]

Faced with the challenge of transceiver design over the Poisson channel, we leverage the deep-learning technique and devise two novel end-to-end learning schemes to fulfill the design task in this paper. One of the schemes accords with the basic principle of the currently available autoencoder (AE) but is specially designed for the Poisson channel with the aid of the square root (SR) transform. The other scheme, following a different design philosophy from AE, is developed based on a double neural network (DNN) model, which regards the receiver and the transmitter as two separate networks. By these designs, the end-to-end learning task can be conducted over Poisson channel. Extensive computer simulations reveal that 1) the transceiver learned by the DNN scheme always performs better than or comparably to the currently available artificially designed transceivers, and 2) compared with the transceiver learned by DNN, the transceiver learned by SR-AE suffers performance loss in some cases, but the SR-AE scheme has a lower complexity to compute the loss function and fewer network parameters. This study takes the first step toward applying end-to-end learning techniques in the field of the Poisson channel and lays a foundation for further works on this topic.

Faced with the challenge of transceiver design over the Poisson channel, we leverage the deep-learning technique and devise two novel end-to-end learning schemes to fulfill the design task in this paper. One of the schemes accords with the basic principle of the currently available autoencoder (AE) but is specially designed for the Poisson channel with the aid of the square root (SR) transform. The other scheme, following a different design philosophy from AE, is developed based on a double neural network (DNN) model, which regards the receiver and the transmitter as two separate networks. By these designs, the end-to-end learning task can be conducted over Poisson channel. Extensive computer simulations reveal that 1) the transceiver learned by the DNN scheme always performs better than or comparably to the currently available artificially designed transceivers, and 2) compared with the transceiver learned by DNN, the transceiver learned by SR-AE suffers performance loss in some cases, but the SR-AE scheme has a lower complexity to compute the loss function and fewer network parameters. This study takes the first step toward applying end-to-end learning techniques in the field of the Poisson channel and lays a foundation for further works on this topic. [ABSTRACT FROM AUTHOR]

The paper derives the optimal second-order coding rate for the continuous-time Poisson channel. We also obtain bounds on the third-order coding rate. This is the first instance of a second-order result for a continuous-time channel. The converse proof hinges on a novel construction of an output distribution induced by Wyner’s discretized channel and the construction of an appropriate $\epsilon $ -net of the input probability simplex. While the achievability proof follows the general program to prove the third-order term for non-singular discrete memoryless channels put forth by Polyanskiy, several non-standard techniques—such as new definitions and bounds on the probabilities of typical sets using logarithmic Sobolev inequalities—are employed to handle the continuous nature of the channel. [ABSTRACT FROM AUTHOR]

In this work, we focus on the classical optical channel having Poissonian statistical behavior and propose a novel secrecy coding-based physical layer protocol. Our protocol is different but complementary to both (computationally secure) quantum immune cryptographic protocols and (information theoretically secure) quantum cryptographic protocols. Specifically, our (information theoretical) secrecy coding protocol secures classical digital information bits at photonic level exploiting the random nature of the Poisson channel. It is known that secrecy coding techniques for the Poisson channel based on the classical one-way wiretap channel (introduced by Wyner in 1975) ensure secret communication only if the mutual information to the eavesdropper is smaller than that to the legitimate receiver. In order to overcome such a strong limitation, we introduce a two-way protocol that always ensures secret communication independently of the conditions of legitimate and eavesdropper channels. We prove this claim showing rigorous comparative derivation and analysis of the information theoretical secrecy capacity of the classical one-way and of the proposed two-way protocols. We also show numerical calculations that prove drastic gains and strong practical potential of our proposed two-way protocol to secure information transmission over optical channels. [ABSTRACT FROM AUTHOR]

Information theory provides maximum possible information transfer over communication channels, including neural channels recently emerged as remarkable for disruptive nano-networking applications. Information theory was successfully applied to quantify the ability of biological sensory neurons to transfer the information from dynamic stimuli. However, a little of information theory has been subjected to quantify the reliability of neuro-transmission between synaptically coupled neurons. Neuro-transmission, regarded as molecular synaptic communication, relays information between neurons and significantly affects the overall brain processing performance. In this study, we use concepts from information theory to provide the framework based on closed-form expressions that quantify the information rate allowing assessment of neuro-transmission when the parameters are provided for any type of neurons. Considering Poissonian statistics and the rate coding model of neural communication, we show how the information transferred between cortical neurons depend on the molecular, physiological and morphological diversity of cells, the firing rate, and the synaptic wiring. With synaptic redundancy, we infer the ability of an isolated post-synaptic neuron to reliably convey information encoded in the spike train from a pre-synaptic neuron. Estimating information rate between neurons primarily serves in the evaluation of the overall performance of biological neural nano-networks and the development of artificial nano-networks. [ABSTRACT FROM AUTHOR]

This paper studies the continuous-time Poisson channel whose dark current is random and may change for every $\tau $ -second time interval, where the actual values of the dark current are known to the transmitter as channel-state information (CSI). In the limit where $\tau $ tends to zero, the capacity gain provided by both causal and noncausal CSI is shown to vanish linearly with $\tau $ , so CSI at the transmitter provides almost no capacity improvement. The paper also considers a related problem of the state-dependent very noisy channel. In the very noisy limit, the capacity gain provided by noncausal CSI is shown to be the same as that provided by causal CSI. [ABSTRACT FROM AUTHOR]

We study two-receiver Poisson channels using tools derived from stochastic calculus. We obtain a general formula for the mutual information over the Poisson channel that allows for conditioning and the use of auxiliary random variables. We then use this formula to compute necessary and sufficient conditions under which one Poisson channel is less noisy and/or more capable than another, which turn out to be distinct from the conditions under which this ordering holds for the discretized versions of the channels. We also use a general formula to determine the capacity region of the more capable Poisson broadcast channel with independent message sets, the more capable Poisson wiretap channel, and the general two-decoder Poisson broadcast channel with degraded message sets. [ABSTRACT FROM AUTHOR]

New capacity upper bounds are presented for the discrete-time Poisson channel with no dark current and an average-power constraint. These bounds are a consequence of techniques developed for the seemingly unrelated problem of upper bounding the capacity of binary deletion and repetition channels. Previously, the best known capacity upper bound in the regime where the average-power constraint does not approach zero was due to Martinez (JOSA B, 2007), which is re-derived as a special case of the framework developed in this paper. Furthermore, this framework is carefully instantiated in order to obtain a closed-form bound that improves the result of Martinez everywhere. Finally, capacity-achieving distributions for the discrete-time Poisson channel are studied under an average-power constraint and/or a peak-power constraint and arbitrary dark current. In particular, it is shown that the support of the capacity-achieving distribution under an average-power constraint must only be countably infinite. This settles a conjecture of Shamai (IEE Proceedings I, 1990) in the affirmative. Previously, it was only known that the support must be an unbounded set. [ABSTRACT FROM AUTHOR]

This paper studies the discrete-time Poisson channel and the noiseless binary channel where, after recording a 1, the channel output is stuck at 0 for a certain period; this period is called the “dead time.” The communication capacities of these channels are analyzed, with main focus on the regime where the allowed average input power is close to zero, either because the bandwidth is large, or because the available continuous-time input power is low.

Fundamental relations between information and estimation have been established in the literature for the continuous time Gaussian and Poisson channels. In this paper, we demonstrate that such relations hold for a much larger family of continuous-time channels. We introduce the family of semi-martingale channels where the channel output is a semi-martingale stochastic process, and the channel input modulates the characteristics of the semi-martingale. For these channels, which includes as a special case the continuous time Gaussian and Poisson models, we establish new representations relating the mutual information between the channel input and output to an optimal causal filtering loss, thereby unifying and considerably extending results from the Gaussian and Poisson settings. Extensions to the setting of mismatched estimation are also presented where the relative entropy between the laws governing the output of the channel under two different input distributions is equal to the cumulative difference between the estimation loss incurred by using the mismatched and optimal causal filters, respectively. The main tool underlying these results is the Doob–Meyer decomposition of a class of sub-martingales. The results in this paper can be viewed as the continuous-time analogues of recent generalizations for relations between information and estimation for discrete-time Lévy channels. [ABSTRACT FROM AUTHOR]

Due to the scattering events in the ultra-violet (UV) spectrum, UV optical wireless communication enables transmission from a single transmitter to multiple receivers, which forms a broadcast channel. In this paper, we address the transmission protocols for such a broadcast channel. Based on the degradeness of such a broadcast channel, we propose time-dilation and time-shrinking superposition transmission. For the former, the duration of the base-layer symbols is shorter than that of the enhancement-layer symbols; while for the latter, the duration of the base-layer symbols is longer. We propose the signal detection of the two types of superposition transmission protocols, analyze the detection error probability and the associated upper bounds, and formulate the problems of power allocation among the signal layers. The numerical results on the sum power obtained from the exact detection error probability and the upper bounds are provided. It is shown that for the superposition with two signal layers, the sum power based on the upper bounds can be close to that based on the exact detection error probability. [ABSTRACT FROM AUTHOR]

We study non-coherent detection schemes for molecular communication (MC) systems with negligible inter-symbol interference that do not require knowledge of the channel state information (CSI). In particular, we first derive the optimal maximum likelihood (ML) multiple-symbol (MS) detector for MC systems. As a special case of the optimal MS detector, we show that the optimal ML symbol-by-symbol (SS) detector can be equivalently written in the form of a threshold-based detector, where the optimal decision threshold is constant and depends only on the statistics of the MC channel. The main challenge of the MS detector is the complexity associated with the calculation of the optimal detection metric. To overcome this issue, we propose an approximate MS detection metric that can be expressed in closed form. In addition, we develop a non-coherent decision-feedback detector, which introduces a lower detection delay compared with the optimal MS detector, and a suboptimal blind detector, which has a significantly lower complexity than the optimal MS detector. Finally, we derive analytical expressions for the bit error rate (BER) of the optimal SS detector, as well as upper and lower bounds for the BER of the optimal MS detector. Simulation results confirm the analysis and reveal the effectiveness of the proposed optimal and suboptimal detection schemes compared with the benchmark scheme that assumes perfect CSI knowledge, particularly, when the number of observations used for detection is sufficiently large. Simulation results are also presented that show the performance of the proposed detectors, when inter-symbol interference is non-negligible. [ABSTRACT FROM AUTHOR]

We address full-duplex relaying for optical wireless communication with stochastic and non-negligible self-interference at the relay node with on-off keying modulation. Assume a relay communication system with a source node, a relaying node, and a destination node, where there is no interference at the source and destination nodes, since they merely transmit or receive. We propose two full-duplex relay protocols, namely, the detect-and-forward relaying protocol and the decode-and-forward relay protocol. For the former, the relay node performs the symbol detection and forwards the detected symbols to the destination node, and for the latter, the relay node performs block decoding and forwards the re-encoded block to the destination node. We prove that for sufficiently high self-interference intensity, the achievable transmission rate of the full-duplex relay system can be lower than that of the half-duplex relay system without self-interference for the detect-and-forward relay protocol. We also prove that for the decode-and-forward relay protocol, the full-duplex relay system always has a larger transmission rate compared with the half-duplex relay system. This can be justified by the fact that, for the decode-and-forward protocol, the source-relay link adopts two codebooks in the scenarios with and without self-interference. The performance of the two proposed protocols is evaluated by the numerical results. [ABSTRACT FROM PUBLISHER]

This paper investigates the design of non-binary protograph low-density parity-check codes for the Poisson channel with $m$ -ary pulse position modulation. The field order over which the code is constructed is matched to the pulse position modulation order yielding a coded modulation scheme. The optimization of the low-density parity-check code structure is performed via protograph density evolution on a surrogate $m$ -ary erasure channel. The surrogate design is illustrated to be not only accurate, but also robust for a range of practical values of channel background noise and various modulation orders. As a result the proposed codes show excellent performance over the Poisson channel with pulse position modulation outperforming competing schemes. As a side-product of this paper, finite-length benchmarks on the block error probability are provided, together with a union bound to characterize the code performance in the error floor region. [ABSTRACT FROM AUTHOR]

Fundamental relations between information and estimation have been established in the literature for the discrete-time Gaussian and Poisson channels. In this paper, we demonstrate that such relations hold for a much larger class of observation models. We introduce the natural family of discrete-time Lévy channels where the distribution of the output conditioned on the input is infinitely divisible. For Lévy channels, we establish new representations relating the mutual information between the channel input and output to an optimal expected estimation loss, thereby unifying and considerably extending results from the Gaussian and Poisson settings. We demonstrate the richness of our results by working out two examples of Lévy channels, namely the gamma channel and the negative binomial channel, with corresponding relations between information and estimation. Extensions to the setting of mismatched estimation are also presented. [ABSTRACT FROM AUTHOR]

Neuronal communication is a biological phenomenon of the central nervous system that influences the activity of all intra-body nano-networks. The implicit biocompatibility and dimensional similarity of neurons with miniature devices make their interaction a promising communication paradigm for nano-networks. To understand the information transfer in neuronal networks, there is a need to characterize the noise sources and unreliability associated with different components of the functional apposition between two cells—the synapse. In this paper, we introduce analogies between the optical communication system and the neuronal communication system to apply results from optical Poisson channels in deriving theoretical upper bounds on the information capacity of both the bipartite and tripartite synapses. The latter refer to the anatomical and functional integration of two communicating neurons and surrounding glia cells. The efficacy of information transfer is analyzed under different synaptic setups with progressive complexity, and is shown to depend on the peak rate of the communicated spiking sequence and neurotransmitter (spontaneous) release, neurotransmitter propagation, and neurotransmitter binding. The results provided serve as a progressive step in the evaluation of the performance of neuronal nano-networks and the development of new artificial nano-networks. [ABSTRACT FROM AUTHOR]

The Poisson interference channel, which models optical communication systems with multiple transceivers in the short-noise-limited regime, is investigated. Conditions for the strong interference regime are characterized and the corresponding capacity region is derived, which is the same as that of the compound Poisson multiple-access channel with each receiver decoding both messages. For the cases when the strong interference conditions are not satisfied, inner and outer bounds on the capacity region are derived. The inner bound is derived via approximating the Poisson interference channel by a binary interference channel and then evaluating the corresponding Han–Kobayashi region. The outer bounds are obtained via various techniques, including noise reduction, genie-aided scheme, and channel transformation. The Poisson Z-interference channel is then studied. The sum rate capacity is obtained when the cross link coefficient is either sufficiently small or sufficiently large. [ABSTRACT FROM PUBLISHER]

A notion of directed information between two continuous-time processes is proposed. A key component in the definition is taking an infimum over all possible partitions of the time interval, which plays a role no less significant than the supremum over “space” partitions inherent in the definition of mutual information. Properties and operational interpretations in estimation and communication are then established for the proposed notion of directed information. For the continuous-time additive white Gaussian noise channel, it is shown that Duncan's classical relationship between causal estimation error and mutual information continues to hold in the presence of feedback upon replacing mutual information by directed information. A parallel result is established for the Poisson channel. The utility of this relationship is demonstrated in computing the directed information rate between the input and output processes of a continuous-time Poisson channel with feedback, where the channel input process is constrained to be constant between events at the channel output. Finally, the capacity of a wide class of continuous-time channels with feedback is established via directed information, characterizing the fundamental limit on reliable communication. [ABSTRACT FROM AUTHOR]

Optical and near-optical band communication systems are known to be intrinsically more secure than comparable RF channels, due to their narrow beamwidths and, in some cases, their high atmospheric absorption. The use of coding against wiretapping for such channels is investigated. For the degraded Poisson wiretap channel model, the secrecy capacity is determined exactly. Moreover, a complete characterization of the rate-equivocation region is presented. For achievability, an optimal code is constructed explicitly by using a code designed by Wyner for the Poisson channel. The converse is proved in two ways: the first method leverages the low-SNR nature of the channel and relies only on simple properties of conditional expectation and classical information inequalities. The second method uses a link recently established between minimum mean square error estimation and mutual information over Poisson channels. [ABSTRACT FROM AUTHOR]

Let X be a nonnegative random variable and let the conditional distribution of a random variable Y, given X, be Poisson (\gamma\cdot X), for a parameter \gamma\geq 0. We identify a natural loss function such that: 1) the derivative of the mutual information between X and Y with respect to \gamma is equal to the minimum mean loss in estimating X based on Y, regardless of the distribution of X; 2) when X\sim P is estimated based on Y by a mismatched estimator that would have minimized the expected loss had X\sim Q, the integral over all values of \gamma of the excess mean loss is equal to the relative entropy between P and Q. For a continuous time setting where X is a nonnegative stochastic process and the conditional law of Y, given X, is that of a non-homogeneous Poisson process with intensity function \gamma\cdot X, under the same loss function: 1) the minimum mean loss in causal filtering when \gamma=\gamma0 is equal to the expected value of the minimum mean loss in noncausal filtering (smoothing) achieved with a channel whose parameter \gamma is uniformly distributed between 0 and \gamma0. Bridging the two quantities is the mutual information between X and Y; 2) this relationship between the mean losses in causal and noncausal filtering holds also in the case where the filters employed are mismatched, i.e., optimized assuming a law on X which is not the true one. Bridging the two quantities in this case is the sum of the mutual information and the relative entropy between the true and the mismatched distribution of Y. Thus, relative entropy quantifies the excess estimation loss due to mismatch in this setting. These results are parallel to those recently found for the Gaussian channel: the I-MMSE relationship of Guo , the relative entropy and mismatched estimation relationship of Verdú, and the relationship between causal and noncasual mismatched estimation of Weissman. [ABSTRACT FROM AUTHOR]

The asymptotic capacity at low input powers of an average-power limited or an average- and peak-power limited discrete-time Poisson channel is considered. For a Poisson channel whose dark current is zero or decays to zero linearly with its average input power \cal E, capacity scales like \cal E\log1\over\cal E for small \cal E. For a Poisson channel whose dark current is a nonzero constant, capacity scales, to within a constant, like \cal E\log\log1\over\cal E for small \cal E. [ABSTRACT FROM AUTHOR]

One of the difficulties in calculating the capacity of certain Poisson channels is that H(λ), the entropy of the Poisson distribution with mean λ, is not available in a simple form. In this paper, we derive upper and lower bounds for H(λ) that are asymptotically tight and easy to compute. The derivation of such bounds involves only simple probabilistic and analytic tools. This complements the asymptotic expansions of Knessl (1998), Jacquet and Szpankowski (1999), and Flajolet (1999). The same method yields tight bounds on the relative entropy D(n,p) between a binomial and a Poisson, thus refining the work of Harremoës and Ruzankin (2004). Bounds on the entropy of the binomial also follow easily. [ABSTRACT FROM AUTHOR]

The large-inputs asymptotic capacity of a peak-power and average-power limited discrete-time Poisson channel is derived using a new firm (nonasymptotic) lower bound and an asymptotic upper bound. The upper bound is based on the dual expression for channel capacity and the notion of capacity-achieving input distributions that escape to infinity. The lower bound is based on a lower bound on the entropy of a conditionally Poisson random variable in terms of the differential entropy of its conditional mean. [ABSTRACT FROM AUTHOR]

A conceptually simple proof for the capacity formula of an exponential server timing channel is provided. The proof links the timing channel to the point-process channel with instantaneous noiseless feedback. This point-process approach enables a study of timing channels that arise in multiserver queues, queues in tandem, and other simple configurations. Although the capacities of such channels remain to be found, the paper provides some analytical bounds and highlights a method to find achievable rates via simulations. [ABSTRACT FROM AUTHOR]

Several models of wide-band broadcast communication scenarios are studied with an emphasis on conditions under which, as the bandwidth tends to infinity, time sharing is asymptotically optimal. The models include the Gaussian channel, the Poisson channel, the "very noisy" channel, and the average-power limited fading channel. Only stochastically degraded scenarios are studied. [ABSTRACT FROM AUTHOR]

This paper studies the discrete-time Poisson channel and the noiseless binary channel where, after recording a 1, the channel output is stuck at 0 for a certain period; this period is called the "dead time." The communication capacities of these channels are analyzed, with main focus on the regime where the allowed average input power is close to zero, either because the bandwidth is large, or because the available continuous-time input power is low. [ABSTRACT FROM AUTHOR]

Deep space Free Space Optical (FSO) systems are an emerging technology, characterized with very high performance in terms of information capacity and communication distances. However, this novel technology demands preliminary experiments, which are unmanageable in real conditions. Regarding this issue, an innovative approach for testing deep space optical communication links in controlled laboratory environment is developed. The proposed testbed is based on fibre optics technology and combines a couple of units, which represent a real deep space FSO link. The parameters of several different photon-counting receivers are discussed. Similar to already demonstrated deep space missions, the implemented optical receiver is Superconducting Nanowire Single Photon Detector (SNSPD) characterized with high single photon sensitivity and detection efficiency. Consequently, in this paper an authentic deep space Poisson channel is theoretically defined, emulated and examined. The description of the Poisson point process is supported by real SNSPD measurements in terms of high efficiency single-photon detection. Moreover, a self-developed unit including Variable Optical Attenuator (VOA) and software controlling the attenuation in the communication loop of the breadboard is applied. Apart from possibility for representing atmospheric-induced fading due to turbulence and Mie scattering effects, the module is utilized as a precise attenuator changing dynamically the optical power based on lookup tables. Addressing the capabilities of VOA unit, detection intensity, detector noise and efficiency versus SNSPD's bias current is measured. In addition, VOA is used as a very slow optical modulator for testing the restrictions regarding On-Off extinction ratio of the implemented SNSPD detector. [ABSTRACT FROM AUTHOR]