Channel State Sequence Research Articles

Efficient Prediction of Link Outage in Mobile Optical Wireless Communications

Optical wireless networks, especially those relying on visible light communications, suffer from severe deterioration in signal's quality when the line-of-sight (LOS) link is absent due to user's mobility. In order to enable efficient resource management within such networks, reliable prediction of LOS link outage is essential. Towards this objective, this article proposes a data-driven approach based on deep machine learning techniques to predict the outage events in an LOS link. First, we present a framework to generate sufficient data representing the channel gain in mobile optical wireless networks that consist of visible light communications in the downlink and infrared communications in the uplink. Using the developed dataset, we propose a channel predictor that forecasts the burst outages or signal recoveries in the upcoming frames using a deep recurrent neural network that implements long-short-term-memory (LSTM) units. To achieve this goal, we propose a low-complexity approach to reduce the data sparsity due to the user's mobility by abstracting and densifying the channel state sequence. For a one second prediction interval, the proposed prediction framework achieves an event hit rate of 91.55% for abrupt outages with an average event timing error of 79 ms, and 83.19% for recoveries from outages with 145 ms timing error. This timing error is on the same order of magnitude with the coherence time of the optical wireless channel. Therefore, this predictor is very useful in developing efficient resource management strategies in such optical networks.

On Capacity of the Writing Onto Fast Fading Dirt Channel

The "Writing onto Fast Fading Dirt" (WFFD) channel is investigated to study the effects of partial channel knowledge on the capacity of the "writing on dirty paper" channel. The WFFD channel is the Gel'fand-Pinsker channel in which the output is obtained as the sum of the input, white Gaussian noise and a fading-times-state term. The fading-times-state term is equal to the element-wise product of the channel state sequence, known only at the transmitter, and a fast fading process, known only at the receiver. We consider the case of Gaussian distributed channel states and derive an approximate characterization of capacity for different classes of fading distributions, both continuous and discrete. In particular, we prove that if the fading distribution concentrates in a sufficiently small interval, then capacity is approximately equal to the AWGN capacity times the probability of this interval. We also show that there exists a class of fading distributions for which having the transmitter treat the fading-times-state term as additional noise closely approaches capacity. Although a closed-form expression of the capacity of the general WFFD channel remains unknown, our results show that the presence of fading can severely reduce the usefulness of channel state knowledge at the transmitter.

On Capacity of the Writing Onto Fast Fading Dirt Channel

The Writing onto Fast Fading Dirt (WFFD) channel is investigated to study the effect of partial channel knowledge on the performance of interference pre-cancellation. The WFFD channel is the Gel’fand-Pinsker channel in which the channel output is the sum of the channel input, white Gaussian noise, and a fading-times-state term. The fading-times-state term is obtained as the product of the channel state sequence, known only at the transmitter, and a fast fading process, known only at the receiver. We consider the case of Gaussian-distributed channel states and derive an approximate characterization of capacity for different classes of fading distributions, both continuous and discrete. In particular, we prove that if the fading distribution concentrates in a sufficiently small interval, then capacity is approximately equal to the AWGN capacity times the probability of such interval. We also show that there exists a class of fading distributions for which having the transmitter treat the fading-times-state term as additional noise closely approaches capacity.

Performance Analysis of Opportunistic Distributed Scheduling In Multi-User Systems

Consider the problem of a multiple access channel in a time dependent environment with a large number of users. In such a system, mostly due to practical constraints (e.g., decoding complexity), not all users can be scheduled together, and usually only one user may transmit at any given time. Assuming a distributed, opportunistic scheduling algorithm, we analyse the system's properties, such as delay, QoS and capacity scaling laws. Specifically, we start with analyzing the performance while \emph{assuming the users are not necessarily fully backlogged}, focusing on the queueing problem and, especially, on the \emph{strong dependence between the queues}. We first extend a known queueing model by Ephremides and Zhu, to give new results on the convergence of the probability of collision to its average value (as the number of users grows), and hence for the ensuing system performance metrics, such as throughput and delay. This model, however, is limited in the number of users one can analyze. We thus suggest a new model, which is much simpler yet can accurately describes the system behaviour when the number of users is large. We then proceed to the analysis of this system under the assumption of time dependent channels. Specifically, we assume each user experiences a different channel state sequence, expressing different channel fluctuations (specifically, the Gilbert-Elliott model). The system performance under this setting is analysed, along with the channel capacity scaling laws.

Arbitrarily Varying Wiretap Channels With Type Constrained States

Determining a single-letter secrecy-capacity formula for the arbitrarily varying wiretap channel (AVWTC) is an open problem largely because of two main challenges. Not only does it capture the difficulty of the compound wiretap channel (another open problem), it also requires that secrecy is ensured with respect to exponentially many possible channel state sequences. By extending the strong soft-covering lemma, recently derived by the authors, to the heterogeneous scenario, this paper accounts for the exponential number of secrecy constraints while only imposing single-letter constraints on the communication rate. Through this approach, we derive a single-letter characterization of the correlated-random (CR)-assisted semantic-security (SS) capacity of an AVWTC with a type constraint on the allowed state sequences. The allowed state sequences are the ones in a typical set around a single constraining type. The stringent SS requirement is established by showing that the mutual information between the message and the eavesdropper’s observations is negligible even when maximized over all message distributions, choices of state sequences, and realizations of the CR-code. Both the achievability and the converse proofs of the type-constrained coding theorem rely on stronger claims than actually required. The direct part establishes a novel single-letter lower bound on the CR-assisted SS-capacity of an AVWTC with state sequences constrained by any convex and closed set of state probability mass functions. This bound achieves the best known single-letter secrecy rates for a corresponding compound wiretap channel over the same constraint set. In contrast to other single-letter results in the AVWTC literature, this paper does not assume the existence of a best channel to the eavesdropper. Instead, SS follows by leveraging the heterogeneous version of the strong soft-covering lemma and a CR-code reduction argument. Optimality is a consequence of a max-inf upper bound on the CR-assisted SS-capacity of an AVWTC with state sequences constrained to any collection of type-classes. When adjusted to the aforementioned compound WTC, the upper bound simplifies to a max–min structure, thus strengthening the previously best known single-letter upper bound by Liang et al. that has a min–max form. The proof of the upper bound uses a novel distribution coupling argument. The capacity formula shows that the legitimate users effectively see an averaged main channel, while security must be ensured versus an eavesdropper with perfect channel state information. An example visualizes our single-letter results, and their relation to the past multi-letter secrecy-capacity characterization of the AVWTC is highlighted.

State Amplification Subject to Masking Constraints

This paper considers a state dependent broadcast channel with one transmitter, Alice, and two receivers, Bob and Eve. The problem is to effectively convey (“amplify”) the channel state sequence to Bob while “masking” it from Eve. The extent to which the state sequence cannot be masked from Eve is referred to as leakage. This can be viewed as a secrecy problem, where we desire that the channel state itself be minimally leaked to Eve while being communicated to Bob. This paper is aimed at characterizing the tradeoff region between amplification and leakage rates for such a system. An achievable coding scheme is presented, wherein the transmitter transmits a partial state information over the channel to facilitate the amplification process. For the case when Bob observes a stronger signal than Eve, the achievable coding scheme is enhanced with secure refinement. Outer bounds on the tradeoff region are also derived, and used in characterizing some special case results. In particular, the optimal amplification-leakage rate difference, called as differential amplification capacity, is characterized for the reversely degraded discrete memoryless channel, the degraded binary, and the degraded Gaussian channels. In addition, for the degraded Gaussian model, the extremal corner points of the tradeoff region are characterized, and the gap between the outer bound and achievable rate-regions is shown to be less than half a bit for a wide set of channel parameters.

On the secrecy of the cognitive interference channel with partial channel states

AbstractThe secrecy problem in the state‐dependent cognitive interference channel is considered in this paper. In our model, there are a primary and a secondary (cognitive) transmitter–receiver pairs, in which the cognitive transmitter has the message of the primary one as side information. In addition, the channel is affected by two channel state sequences, which are known at the cognitive transmitter and the corresponding receiver, separately. The cognitive transmitter should cooperate with the primary one, and it wishes to keep its message secure at the primary receiver. The achievable equivocation‐rate regions for this channel are derived using two approaches: the binning scheme coding and superposition coding. Then, comparisons between the results using two coding approaches, in Gaussian case, shows that there is a trade‐off between the achievable rate of the primary transmitter and the equivocation rate of the cognitive one. Moreover, the outer bounds on the capacity of the channel are derived. Copyright © 2016 John Wiley & Sons, Ltd.

MAC With Action-Dependent State Information at One Encoder

The growing interest in action-dependent channels motivates us to extend the study of action-dependent settings, which until now focused on point-to-point models, to multiple-access channels (MACs). In this paper, we consider a two-user, state-dependent MAC, in which one of the encoders, called the informed (cognitive) encoder, is allowed to take an action that affects the formation of the channel states. Two independent messages are to be sent through the channel: (1) a common message known to both encoders and (2) a private message known only to the informed encoder. In addition, the informed encoder has access to the sequence of channel states in a noncausal manner. Our framework generalizes the previously evaluated settings of state-dependent point-to-point channels with actions and MACs with common messages. We derive a single letter characterization of the capacity region for this setting. Using this general result, we obtain and compute the capacity region for the Gaussian action-dependent MAC. The special methods used in solving the Gaussian case are then applied to obtain the capacity of the Gaussian action-dependent point-to-point channel, a problem left open and solved now. Finally, we establish some dualities between action-dependent channel coding and source coding problems. In particular, we obtain a duality equivalence between the considered MAC setting and the rate distortion model known as successive refinement with actions. This is done by developing a set of simple duality principles that enables us to successfully evaluate the outcome of one problem given the outcome of the other.

Coding Across Finite Transport Blocks in Modern Wireless Communication Systems

Future wireless communications will face the dual challenge of supporting large traffic volume while providing reliable service for various kinds of delay-sensitive traffic. In light of this challenge, this paper investigates the throughput performance of wireless communication systems under channel coding over finite transport blocks (TBs) with the channel state sequence available only at the receiver. When we apply coding across multiple TBs, the underlying wireless channel can be effectively modeled as a finite-state Markov chain. By linking the characteristics of the underlying physical channel to the parameters of the Markov chain, we characterize the channel dispersion of the corresponding system. The channel dispersion is then used to assess the coding performance of various communication strategies. We also propose a communication scheme where the receiver determines the decoding time based on the available channel state sequence. Numerical results show that the proposed scheme significantly reduces the coding delay to achieve rates near the channel capacity.

On the capacity of state-dependent Gaussian cognitive interference channel

A Gaussian cognitive interference channel with state (G-CICS) is studied. In this paper, we focus on the two-sender, two-receiver case and consider the communication situation in which two senders transmit a common message to two receivers. Transmitter 1 knows only message W1, and transmitter 2, referred to as the cognitive user, knows both messages W1 and W2 and also the channel’s states sequence non-causally. Receiver 1 needs to decode only W1 while receiver 2 needs to decode both messages. In this paper, we investigate the weak and moderate interference case where we assume that the channel gain a satisfies |a|≤1. In addition, inner and outer bounds on the capacity region are derived in the regime of high state power, i.e., the channel state sequence has unbounded variance. First, we show that the achievable rate by Gelfand-Pinsker coding vanishes in the high state power regime under a condition over the channel gain. In contrast, we propose a transmission scheme (based on lattice codes) that can achieve positive rates, independent of the interference. Our transmission scheme can achieve the capacity region in a high signal-to-noise ratio (SNR) regime. Also, regardless of all channel parameters, the gap between the achievable rate region and the outer bound is at most 0.5 bits.

MIMO Wiretap Channels With Unknown and Varying Eavesdropper Channel States

In this paper, a class of information theoretic secrecy problems is addressed where the eavesdropper channel state is completely unknown to the legitimate parties. In particular, a Gaussian MIMO wiretap channel is considered, where the eavesdropper channel state can vary from one channel use to the next, and the overall channel state sequence is known only to the eavesdropper. When the eavesdropper has fewer antennas than the transmitter and its intended receiver, a positive secrecy rate in the sense of strong secrecy is proved to be achievable and shown to match with the converse in secure degrees of freedom. This yields the conclusion that secure communication is possible regardless of the location or channel states of the eavesdropper. Additionally, it is observed that, the present setting renders the secrecy capacity problems for some multiterminal wiretap-type channels more tractable as compared to the case with full or partial knowledge of eavesdropper channel states. To demonstrate this observation, secure degrees of freedom regions are derived for the Gaussian MIMO multiple access (MAC) wiretap channel and the two-user Gaussian MIMO broadcast (BC) wiretap channel, where the transmitter(s) and intended receiver(s) have the same number of antennas.

Second-Order Coding Rates for Channels With State

We study the performance limits of state-dependent discrete memoryless channels with a discrete state available at both the encoder and the decoder. We establish the epsilon-capacity as well as necessary and sufficient conditions for the strong converse property for such channels when the sequence of channel states is not necessarily stationary, memoryless or ergodic. We then seek a finer characterization of these capacities in terms of second-order coding rates. The general results are supplemented by several examples including i.i.d. and Markov states and mixed channels.

Upper Bounds on the Capacities of Noncontrollable Finite-State Channels With/Without Feedback

Noncontrollable finite-state channels (FSCs) are FSCs in which the channel inputs have no influence on the channel states, i.e., the channel states evolve freely. Since single-letter formulas for the channel capacities are rarely available for general noncontrollable FSCs, computable bounds are usually utilized to numerically bound the capacities. In this paper, we take the delayed channel state as part of the channel input and then define the directed information rate from the new channel input (including the source and the delayed channel state) sequence to the channel output sequence. With this technique, we derive a series of upper bounds on the capacities of noncontrollable FSCs with/without feedback. These upper bounds can be achieved by conditional Markov sources and computed by solving an average reward per stage stochastic control problem (ARSCP) with a compact state space and a compact action space. By showing that the ARSCP has a uniformly continuous reward function, we transform the original ARSCP into a finite-state and finite-action ARSCP that can be solved by a value iteration method. Under a mild assumption, the value iteration algorithm is convergent and delivers a near-optimal stationary policy and a numerical upper bound.

Bidirectional Broadcast Channel With Random States Noncausally Known at the Encoder

In this work, coding for a discrete memoryless broadcast channel with random states and two receivers is studied. Each receiver knows one of the two information messages at the sender and wants to know the other one. Assuming the channel state sequence is noncausally known at the sender, an achievable rate region based on the Gel'fand–Pinsker coding strategy is derived and an outer bound to the capacity region is presented. Further, the capacity region for the special case where in addition one receiver knows the channel state is established. An equivalent characterization of an achievable rate region characterizing convex set is derived using Shannon's concept of transmit strategies. This characterization is used to derive an Arimoto–Blahut-like algorithm including a stopping criterion to compute the weighted rate-sum maxima, which can be used to characterize the whole achievable rate region. The tradeoff between the input distribution and the impact of the channel state, the necessity of the time-sharing operation, and the additive Gaussian channel case assuming Costa's choice of auxiliary random variables are discussed by examples.

A novel approach to model the land mobile satellite channel through reversible jump markov chain monte carlo technique

A key issue in the design of a mobile satellite communication system is an adequate knowledge of the statistical behavior of the propagation channel. To achieve this goal, the development of very accurate models plays a very important role. In contrast to traditional multi-state Markov chain based models, the novel approach proposed in this paper makes no prior assumptions on the number of states or on the statistical distributions characterizing each state. The sequence of channel states is blindly estimated using a Reversible Jump Monte Carlo Markov Chain algorithm.

On Channels With Partial Channel State Information at the Transmitter

We consider an independent and identically distributed (i.i.d.) state-dependent channel with partial (rate-limited) channel state information (CSI) at the transmitter (CSIT) and full CSI at the receiver (CSIR). The CSIT comprises two parts, both subject to a rate constraint, and communicated over noiseless way-side channels. The first part is coded CSI, provided by a third party (a genie), and the second part is the output of a deterministic scalar quantizer of the state. A single-letter expression for the capacity of the channel is given. In case the CSIT comprises only the coded part, we show that the capacity previously suggested in the literature is too optimistic, and suggest a correction as part of our expression for the information capacity. For the general setting, an optimal coding scheme based upon multiplexing of several codebooks is presented. It is proved that the capacity of the channel is the same whether the quantizer's output is observed causally or noncausally by the encoder. In the rest of the correspondence, we focus on the special case where the system employs a stationary quantizer. Using a rate distortion approach we bound the alphabet's size of the auxiliary random variable (RV) of the information capacity. Next, we turn to the additive white Gaussian noise (AWGN) channel with fading, and show that the determination of the capacity region reduces to finding the optimal genie strategies and the optimal power allocation distribution along the product alphabet of the auxiliary RV and the quantizer's output alphabets. The suggested model can be applied, for example, to an orthogonal frequency-division multiplexing (OFDM) communication system. Here the fading across frequencies comprises the channel state sequence. Coded fading information is provided to the channel encoder via a way-side, rate-limited channel. In addition, since coding operation is expensive, a simpler scheme provides the channel encoder with quantized fading information, e.g., whether each coefficient is above/below a threshold.

Joint parameter estimation and symbol detection for linear or nonlinear unknown channels

We present an iterative method for joint channel parameter estimation and symbol selection via the Baum-Welch algorithm, or equivalently the Expectation-Maximization (EM) algorithm. Channel parameters, including noise variance, are estimated using a maximum likelihood criterion. The Markovian properties of the channel state sequence enable us to calculate the required likelihood using a forward-backward algorithm. The calculated likelihood functions can easily give optimum decisions on information symbols which minimize the symbol error probability. The proposed receiver can be used for both linear and nonlinear channels. It improves the system throughput by making saving in the transmission of known symbols, usually employed for channel identification. Simulation results which show fast convergence are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Convolutional coding for finite-state channels

We propose new decoders for decoding convolutional codes over finite-state channels. These decoders are sequential and utilize the information about the channel state sequence contained in the channel output sequence. The performance of these decoders is evaluated by simulation and compared to the performance of memoryless decoders with and without interleaving. Our results show that the performance of these decoders is good whenever the channel statistics are such that the joint estimate of the channel state sequence and the channel input sequence is good, as, for example, when the channel is bursty. In these cases using even a partial search decoder such as the Fano decoder over the appropriate trellis is nearly optimal. However, when the information between the output sequence and the sequence of channel states and inputs diminishes, the memoryless decoder with interleaving outperforms even the optimal decoder which knows the channel state.

Splitting algorithms in channels with markovian capture

AbstractIn this paper we study the performance of tree‐like splitting collision resolution algorithms in channels with markovian capture. In particular, we assume that in each slot the channel can be in one of two states ‐b (for “bad”) and g (for “good”). When the channel is in state b, a capture can never occur. When the channel is in state g and n nodes (n ≥ 2) are transmitting, a capture occurs with probability πn. The sequence of channel states is assumed to be a homogeneous Markov chain. We derive the throughput of a splitting tree‐like multiple access algorithm for this channel. We also provide simulation results for the average delay.

The capacity of the arbitrarily varying channel revisited: positivity, constraints

A well-known result of R. Ahlswede (1970) asserts that the deterministic code capacity of an arbitrarily varying channel (AVC), under the average-error-probability criterion, either equals its random code capacity or else is zero. A necessary and sufficient condition is identified for deciding between these alternative, namely, the capacity is zero if and only if the AVC is symmetrizable. The capacity of the AVC is determined with constraints on the transmitted codewords as well as on the channel state sequences, and it is demonstrated that it may be positive but less than the corresponding random code capacity. A special case of the results resolves a weakened version of a fundamental problem of coding theory. >