We discuss the problem of designing channel access architectures for enabling fast, low-latency, grant-free and uncoordinated uplink for densely packed wireless nodes. Specifically, we extend the concept of random-access code introduced at ISIT'2017 by one of the authors to the practically more relevant case of the AWGN multiple-access channel (MAC) subject to Rayleigh fading, unknown to the decoder. We derive bounds on the fundamental limits of random-access coding and propose an alternating belief-propagation scheme as a candidate practical solution. The latter's performance was found to be surprisingly close to the information-theoretic bounds. It is curious, thus, that while fading significantly increases the minimal required energyper-bit E b /N0 (from about 0-2 dB to about 8-11 dB), it appears that it is much easier to attain the optimal performance over the fading channel with a practical scheme by leveraging the inherent randomization introduced by the channel. Finally, we mention that while a number of candidate solutions (MUSA, SCMA, RSMA, etc.) are being discussed for the 5G, the informationtheoretic analysis and benchmarking has not been attempted before (in part due to lack of common random-access model). Our work may be seen as a step towards unifying performance comparisons of these methods.
Consider a (multiple-access) wireless communication system where users are connected to a unique base station over a shared-spectrum radio links. Each user has a fixed number k of bits to send to the base station, and his signal gets attenuated by a random channel gain (quasi-static fading). In this paper we consider the many-user asymptotics of Chen-Chen-Guo'2017, where the number of users grows linearly with the blocklength. Differently, though, we adopt a per-user probability of error (PUPE) criterion (as opposed to classical joint-error probability criterion). Under PUPE the finite energyper-bit communication is possible, and we are able to derive bounds on the tradeoff between energy and spectral efficiencies. We reconfirm the curious behaviour (previously observed for non-fading MAC) of the possibility of almost perfect multi-user interference (MUI) cancellation for user densities below a critical threshold. Further, we demonstrate the suboptimality of standard solutions such as orthogonalization (i.e., TDMA/FDMA) and treating interference as noise (i.e. pseudo-random CDMA without multi-user detection). Notably, the problem treated here can be seen as a variant of support recovery in compressed sensing for the unusual definition of sparsity with one non-zero entry per each contiguous section of 2 k coordinates. This identifies our problem with that of the sparse regression codes (SPARCs) and hence our results can be equivalently understood in the context of SPARCs with sections of length 2 100 . Finally, we discuss the relation of the almost perfect MUI cancellation property and the replica-method predictions.Index Terms-Finite blocklength, many-user MAC, per-user probability of error, approximate message passing, replicamethod I. INTRODUCTIONWe clearly witness two recent trends in the wireless communication technology: the increasing deployment density and miniaturization of radio-equipped sensors. The first trend results in progressively worsening interference environment, while the second trend puts ever more stringent demands on communication energy efficiency. This suggests a bleak picture for the future networks, where a chaos of packet collisions and interference contamination prevents reliable connectivity.This paper is part of a series aimed at elucidating the fundamental tradeoffs in this new "dense-networks" regime
Consider a (multiple-access) wireless communication system where users are connected to a unique base station over a shared-spectrum radio links. Each user has a fixed number k of bits to send to the base station, and his signal gets attenuated by a random channel gain (quasi-static fading). In this paper we consider the many-user asymptotics of Chen-Chen-Guo'2017, where the number of users grows linearly with the blocklength. In addition, we adopt a per-user probability of error criterion of Polyanskiy'2017 (as opposed to classical joint-error probability criterion). Under these two settings we derive bounds on the optimal required energy-perbit for reliable multi-access communication. We confirm the curious behaviour (previously observed for non-fading MAC) of the possibility of perfect multi-user interference cancellation for user densities below a critical threshold. Further we demonstrate the suboptimality of standard solutions such as orthogonalization (i.e., TDMA/FDMA) and treating interference as noise (i.e. pseudo-random CDMA without multi-user detection).
We consider the problem of estimating a stochastic linear time-invariant (LTI) dynamical system from a single trajectory via streaming algorithms. The problem is equivalent to estimating the parameters of vector auto-regressive (VAR) models encountered in time series analysis (Hamilton (2020)). A recent sequence of papers (Faradonbeh et al., 2018;Simchowitz et al., 2018;Sarkar and Rakhlin, 2019) show that ordinary least squares (OLS) regression can be used to provide optimal finite time estimator for the problem. However, such techniques apply for offline setting where the optimal solution of OLS is available apriori. But, in many problems of interest as encountered in reinforcement learning (RL), it is important to estimate the parameters on the go using gradient oracle. This task is challenging since standard methods like SGD might not perform well when using stochastic gradients from correlated data points (Györfi and Walk, 1996;Nagaraj et al., 2020).In this work, we propose a novel algorithm, SGD with Reverse Experience Replay (SGD − RER), that is inspired by the experience replay (ER) technique popular in the RL literature (Lin, 1992). SGD − RER divides data into small buffers and runs SGD backwards on the data stored in the individual buffers. We show that this algorithm exactly deconstructs the dependency structure and obtains information theoretically optimal guarantees for both parameter error and prediction error for standard problem settings. Thus, we provide the first -to the best of our knowledge -optimal SGD-style algorithm for the classical problem of linear system identification aka VAR model estimation. Our work demonstrates that knowledge of dependency structure can aid us in designing algorithms which can deconstruct the dependencies between samples optimally in an online fashion.
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