The Poltyrev bound provides a very tight upper bound on the decoding error probability when using binary linear codes for transmission over the binary symmetric channel and the additive white Gaussian noise channel, making use of the code's weight spectrum. In the present work, the bound is extended to symmetric binary-input memoryless channels with a discrete output alphabet. The derived bound is demonstrated on a hybrid BSC-BEC channel. Additionally, a reduced-complexity bound is introduced at the cost of some loss in tightness.

Channel quantization is commonly used in non-volatile memories and front-end communication systems. The general setting of channel quantization and mapping involves two phases: a contiguous quantization subject to a specified number of thresholds, referred to as the threshold-constrained, and a mapping with restricted output cardinality. The latter constraint stems from limitations in data throughput and the complexity of post-processing. The objective of this study is to maximize mutual information at the output of the channel quantization and mapping block. We demonstrate that, given a predefined mapping, an optimal solution exists for threshold-constrained using a dynamic programming algorithm. This approach is particularly suitable for non-volatile memory architectures, where the read process involves establishing read thresholds modeled as contiguous quantization. The incorporation of mapping is crucial due to constraints on output cardinality. Furthermore, we provide sufficient conditions for achieving the global optimal solution to the general channel quantization and mapping problem, which involves joint optimization of contiguous quantization and mapping.

We consider a line network of nodes connected by additive white Gaussian noise channels and equipped with local feedback. We study the velocity at which information spreads over this network. For the transmission of a data packet, we derive an explicit positive lower bound on the velocity for any packet size. Furthermore, we consider streaming, that is, transmission of data packets that is generated at a given average arrival rate. We show that a positive velocity exists as long as the arrival rate is below the individual Gaussian channel capacity and provide an explicit lower bound. Our analysis involves applying pulse-amplitude modulation to the data (successively in the streaming case) and using linear mean-squared error estimation at the network nodes. Due to the analog-linear nature of the scheme, the results extend to any additive noise. For general noise, we derive exponential error-probability bounds. Moreover, for (sub-)Gaussian noise, we show doubly-exponential behavior, which reduces to the celebrated Schalkwijk-Kailath scheme when considering a single node. By viewing the constellation as an “analog source”, we also provide bounds on the exponential decay of the mean-squared error of source transmission over the network.

We consider the problem of in-order packet transmission over a cascade of packet-erasure links with acknowledgment (ACK) signals, interconnected by relays. We treat first the case of transmitting a single packet, in which ACKs are unnecessary, over links with independent identically distributed erasures. For this case, we derive tight upper and lower bounds on the probability of arrive failure within an allowed end-to-end communication delay over a given number of links. When the number of links is commensurate with the allowed delay, we determine the maximal ratio between the two—coined information velocity—for which the arrive-failure probability decays to zero; we further derive bounds on the arrive-failure probability when the ratio is below the information velocity, determine the exponential arrive-failure decay rate, and extend the treatment to links with different erasure probabilities. We then elevate all these results for a stream of packets with independent geometrically distributed interarrival times, and prove that the information velocity and the exponential decay rate remain the same for any stationary ergodic arrival process and for deterministic interarrival times. We demonstrate the significance of the derived fundamental limits—the information velocity and the arrive-failure exponential decay rate—by comparing them to simulation results.

As over-the-air (OTA) vehicle software and firmware updates become a common and frequent practice, Original Equipment Manufacturers (OEMs) seek ways to reduce costs by utilizing opportunistic free wireless links into the vehicle. Unlike previous works that rely on opportunistic direct wireless links between the vehicle and network infrastructure (or other vehicles), our proposed VehiCache system uses trusted mobile devices, e.g., smart phones, as agents, to bridge between the cloud and the vehicle, and seamlessly convey OTA data into the vehicle, in a secure, and a cost effective manner. VehiCache was simulated and analyzed under both the traditional car-ownership, and the popularity-gaining-car-sharing usage models, showing that VehiCache is capable of distributing non-critical OTA content with high probability within a reasonable amount of time. Finally, preliminary observations from a VehiCache proof-of-concept demonstrator are reported.

The wireless channel behaves markedly different depending on whether a line-of-sight (LOS) between transmitter and receiver exists or not. State-of-the-art wireless channel models for mobile communications, such as, IST WINNER-II and the 3GPP 3D channel model specified in TR 36.873, define distinct macro- and microscopic fading parameters for LOS and non-LOS (NLOS) situations. Whether a user is in LOS/NLOS is randomly determined by a distance-dependent LOS probability; commonly, for each position in the network, the random realization of LOS/NLOS propagation is independently determined from this distance-dependent LOS probability. Since in this case random draws of neighboring positions are statistically independent, base station assignment regions in system-level simulations become highly irregular. To mitigate this problem, we propose an efficient method to generate spatially correlated LOS/NLOS channel maps that follow predefined distance-dependent LOS probability and additionally enable spatial clustering of LOS positions.

In this paper, we investigate downlink physical layer multicast transmit beamforming in wireless cellular networks, considering interference between multiple independent multicast transmitters (base stations). Transmit beamforming can exploit multiple antennas at the transmitter to direct the multicast signal towards the intended users, while minimizing the interference leakage caused to other users of the network. We propose a multicast beamformer optimization problem that maximizes the achievable multicast transmission rate while restricting the interference leakage caused to other users, by applying a semidefinite relaxation to approximate this NP-hard problem with a convex optimization problem that can be solved efficiently. Furthermore, we consider multiple receive antennas at the users and propose an antenna combiner that maximizes the achievable user rate. Finally, we combine the proposed beamforming and receive antenna combining methods via alternating optimization and evaluate the performance using Monte-Carlo simulations.

In Costa's dirty-paper channel, Gaussian random binning is able to eliminate the effect of interference which is known at the transmitter, and thus achieve capacity. We examine a generalization of the dirty-paper problem to a multiple access channel (MAC) setup, where structured (lattice-based) binning seems to be necessary to achieve capacity. In the dirty-MAC, two additive interference signals are present, one known to each transmitter but none to the receiver. The achievable rates using Costa's Gaussian binning vanish if both interference signals are strong. In contrast, it is shown that lattice-strategies (“lattice precoding”) can achieve positive rates, independent of the interference power. Furthermore, in some cases-which depend on the noise variance and power constraints-high-dimensional lattice strategies are in fact optimal. In particular, they are optimal in the limit of high SNR-where the capacity region of the dirty MAC with strong interference approaches that of a clean MAC whose power is governed by the minimum of the users' powers rather than their sum. The rate gap at high SNR between lattice-strategies and optimum (rather than Gaussian) random binning is conjectured to be1/2log2(πe/6) ≈ 0.254 bit. Thus, the doubly dirty MAC is another instance of a network setting, like the Körner-Marton problem, where (linear) structured coding is potentially better than random binning.

For general memoryless systems, the existing information-theoretic solutions have a ldquosingle-letterrdquo form. This reflects the fact that optimum performance can be approached by a random code (or a random binning scheme), generated using independent and identically distributed copies of some scalar distribution. Is that the form of the solution of any (information-theoretic) problem? In fact, some counter examples are known. The most famous one is the ldquotwo help onerdquo problem: Korner and Marton showed that if we want to decode the modulo-two sum of two correlated binary sources from their independent encodings, then linear coding is better than random coding. In this paper we provide another counter example, the ldquodoubly-dirtyrdquo multiple-access channel (MAC). Like the Korner-Marton problem, this is a multiterminal scenario where side information is distributed among several terminals; each transmitter knows part of the channel interference while the receiver only observes the channel output. We give an explicit solution for the capacity region of the binary doubly-dirty MAC, demonstrate how this region can be approached using a linear coding scheme, and prove that the ldquobest known single-letter regionrdquo is strictly contained in it. We also state a conjecture regarding the capacity loss of single-letter characterization in the Gaussian case.

For general memoryless systems, the typical information theoretic solution, when exists, has a ldquosingle-letterrdquo form. This reflects the fact that optimum performance can be approached by a random code (or a random binning scheme), generated using independent and identically distributed copies of some single-letter distribution. Is that the form of the solution of any (information theoretic) problem? In fact, some counter examples are known, perhaps the most famous being the Korner-Marton ldquotwo help onerdquo problem, where the modulo-two sum of two binary sources is to be decoded from their independent encodings. In this paper we provide another counter example, the ldquodoubly-dirtyrdquo multiple access channel (MAC). Like the Korner-Marton problem, this example is associated with a multiterminal scenario where side information is distributed among several terminals; each transmitter knows part of the channel interference but the receiver is not aware of any part of it. We give an explicit solution for the capacity region of a binary version of the doubly-dirty MAC, demonstrate how this capacity region can be approached using a linear coding scheme, and prove that the ldquobest known single-letter regionrdquo is strictly contained in it. We also state a conjecture regarding a similar rate loss of single letter characterization in the Gaussian case.

We consider a generalization of the Gaussian dirty- paper problem to a multiple access setup. There are two additive interferences, one known to each transmitter but none to the receiver. The rates achievable using random binning schemes (i.e. schemes based on Costa's auxiliary random variables) vanish in the limit when the interferences are strong. In contrast, we show that lattice strategies ("lattice preceding") can achieve positive rates independent of the interferences. Furthermore, we derive an outer bound for the capacity region for arbitrary interferences, which is strictly smaller than the clean MAC capacity region. We then show that lattice strategies meet this outer bound for some combinations of noise variance and power constraints. In particular, lattice strategies are optimal in the limit of high SNR. Thus, the dirty MAC is another instance of a network setup, like the Korner-Marton modulo-two sum problem, where linear coding is better than random binning. We also derive lattice transmission schemes and conditions for optimality for the asymmetric case, where there is only one interference which is known to one of the users, and in particular for the helper problem, where the user which knows the interference does not have a message it wishes to transmit.

We investigate the capacity loss for using uncorrelated Gaussian input over a multiple-input multiple-output (MIMO) linear additive-noise channel. We upper-bound the capacity loss by a universal constant C* which is independent of the channel matrix and the noise distribution. For a single-user MIMO channel with ntinputs and nroutputs C* = min [ 1/2, nr/ntlog2(1+nt/nr) ] bit per input dimension (or 2C* bit per transmit antenna per second per hertz), under both total and per-input power constraints. If we restrict attention to (colored) Gaussian noise, then the capacity loss is upper-bounded by a smaller constant CG= nr/2nrlog2(nt/nr) for nrges nt/e, and CG= 0.265 otherwise, and this bound is tight for certain cases of channel matrix and noise covariance. We also derive similar bounds for the sum-capacity loss in multiuser MIMO channels. This includes in particular uncorrelated Gaussian transmission in a MIMO multiple-access channel (MAC), and "flat" Gaussian dirty-paper coding (DPC) in a MIMO broadcast channel. In the context of wireless communication, our results imply that the benefit of beamforming and spatial water-filling over simple isotropic transmission is limited. Moreover, the excess capacity of a point-to-point MIMO channel over the same MIMO channel in a multiuser configuration is bounded by a universal constant.

We investigate the sum-capacity loss for using uncorrelated Gaussian inputs over multiple-input multiple-output (MIMO) power-constrained linear additive-noise channels in multi-user configurations. We show that the sum-capacity loss is bounded by a universal constant which depends only on the total number of input and output dimensions of the channel, but is independent of the channel matrix, the noise distribution and the number of users. Specifically, for a multiple-access channel with a total number of nttransmit antennas and base-station with nrreceive antennas, the sum-capacity loss is at most C* = min{1/2, nr/2ntlog2(1 + nt/nr)} bit per input dimension (or 1 bit per transmit antenna per second per Hertz). If we restrict attention to Gaussian noises, then the capacity loss is upper bounded by CG* = min{0.265, 0.265nr/ntlog2(nt/nr)}, and this bound is tight for certain channel matrices and noise spectra. We show also that the same bounds hold for the sum-capacity loss of uncorrelated Gaussian input over linear MIMO broadcast channels, input distribution being interpreted either in terms of the equivalent point-to-point channel with Sato condition, or as the output distribution of a "dirty-paper" transmitter. One implication of these results is the limited value of coherence and water-filling in spatial transmission. Another implication is the limited capacity loss in multi-user configurations relative to the fully cooperative (point-to-point) channel