Improved bounds on Gaussian MAC and sparse regression via Gaussian inequalities

Zadik, Ilias; Polyanskiy, Yury; Thrampoulidis, Christos

doi:10.1109/isit.2019.8849764

Cited by 57 publications

(94 citation statements)

References 22 publications

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“…A. System Model Channel model: In order to capture the scenario of a memoryless Gaussian channel with K possible transmitters, a single receiver, and an unknown activity pattern A ⊆ [K] describing which transmitters are active, we describe the Gaussian RAC by a family of Gaussian MACs (17), each indexed by the number of active transmitters k ∈ {0, . .…”

Section: A Nonasymptotic Bound and Its Analysis For The Gaussian mentioning

confidence: 99%

“…Chen and Guo [14] find the capacity of the Gaussian many access channel, and Chen et al [15] derive the capacity of the Gaussian many access channel in a random access scenario where the number of users K is unknown. For the criterion of average per-user error probability, Polyanskiy [16] and Zadik et al [17] derive non-asymptotic random coding achievability bounds when K transmitters are active. Extensions of these ideas to quasi-static fading MACs and RACs appear in [18] and [19], respectively.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Multiple and Random Access in the Finite Blocklength Regime

Yavas

Kostina

Effros

2020

2020 IEEE International Symposium on Information Theory (ISIT)

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This paper presents finite-blocklength achievability bounds for the Gaussian multiple access channel (MAC) and random access channel (RAC) under average-error and maximal-power constraints. Using random codewords uniformly distributed on a sphere and a maximum likelihood decoder, the derived MAC bound on each transmitter's rate matches the MolavianJazi-Laneman bound (2015) in its first-and secondorder terms, improving the remaining terms to 1 2 log n n + O 1 n bits per channel use. The result then extends to a RAC model in which neither the encoders nor the decoder knows which of K possible transmitters are active. In the proposed rateless coding strategy, decoding occurs at a time nt that depends on the decoder's estimate t of the number of active transmitters k. Single-bit feedback from the decoder to all encoders at each potential decoding time ni, i ≤ t, informs the encoders when to stop transmitting. For this RAC model, the proposed code achieves the same first-, second-, and third-order performance as the best known result for the Gaussian MAC in operation. Index Terms-Gaussian multiple access channel, Gaussian random access channel, third-order asymptotics, finite blocklength, maximum likelihood decoder, spherical distribution.

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Section: A Nonasymptotic Bound and Its Analysis For The Gaussian mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gaussian Multiple and Random Access in the Finite Blocklength Regime

Yavas

Kostina

Effros

2020

2020 IEEE International Symposium on Information Theory (ISIT)

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“…Under PUPE [7], [17] and most recently [18] showed progressively tighter bounds on the fundamental energy-per-bit required for reliable massive access communication. More exactly, those work consider the regime where the number of bits transmitted by each user ("payload") is fixed, and the number of users grows to infinity linearly with the blocklength (so that the density of users per d.o.f., and hence the spectral efficiency, is held constant).…”

Section: Fundamental Limits For Coordinated Massive Accessmentioning

confidence: 99%

“…More exactly, those work consider the regime where the number of bits transmitted by each user ("payload") is fixed, and the number of users grows to infinity linearly with the blocklength (so that the density of users per d.o.f., and hence the spectral efficiency, is held constant). The two key observations of [7], [17], [18] are the following:…”

Section: Fundamental Limits For Coordinated Massive Accessmentioning

confidence: 99%

Massive Access for Future Wireless Communication Systems

Gao

Zhou

et al. 2020

IEEE Wireless Commun.

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178

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Multiple access technology plays an important role in wireless communication in the last decades: it increases the capacity of the channel and allows different users to access the system simultaneously. However, the conventional multiple access technology, as originally designed for current human-centric wireless networks, is not scalable for future machine-centric wireless networks.Massive access (also known as "Massive-device Multiple Access", "Unsourced Massive Random Access", "Massive Connectivity", "Massive Machine-type Communication", and "Many-Access Channels") exhibits a clean break with current networks by potentially supporting millions of devices in each cellular network. The exponential growth in the number of devices requires entirely new "massive" access technologies to tackle the fundamental challenges which needs prompt attention: the fundamental framework of the communication from a massive number of bursty devices transmitting simultaneously with short packets, the low complexity and energy-efficient massive access coding and communication schemes, the effective active user detection method among a large number of potential user devices with sporadic device activity patterns, and the integration of massive access with massive Y. Wu is with the

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“…2], since P (n) e → 0 only if E n → ∞ (see Lemma 1 ahead). Remark 2: Many works in the literature on many-access channels, including [4], [5], [10]- [13], consider a peruser probability of error…”

Section: Remarkmentioning

confidence: 99%

Capacity per Unit-Energy of Gaussian Random Many-Access Channels

Ravi

Koch

2020

2020 IEEE International Symposium on Information Theory (ISIT)

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This paper considers a Gaussian multiple-access channel with random user activity where the total number of users n and the average number of active users k n may grow with the blocklength n. For this channel, it studies the maximum number of bits that can be transmitted reliably per unit-energy as a function of n and k n. When all users are active with probability one, i.e., n = k n , it is demonstrated that if k n is of an order strictly below n/ log n, then each user can achieve the single-user capacity per unit-energy (log e)/N 0 (where N 0 /2 is the noise power) by using an orthogonal-access scheme. In contrast, if k n is of an order strictly above n/ log n, then the users cannot achieve any positive rate per unit-energy. Consequently, there is a sharp transition between orders of growth where interference-free communication is feasible and orders of growth where reliable communication at a positive rate per unit-energy is infeasible. It is further demonstrated that orthogonal codebooks, which achieve the capacity per unit-energy when the number of users is bounded, can be strictly suboptimal. When the user activity is random, i.e., when n and k n are different, it is demonstrated that if k n log n is sublinear in n, then each user can achieve the single-user capacity per unit-energy (log e)/N 0. Conversely, if k n log n is superlinear in n, then the users cannot achieve any positive rate per unit-energy. Consequently, there is again a sharp transition between orders of growth where interference-free communication is feasible and orders of growth where reliable communication at a positive rate is infeasible that depends on the asymptotic behaviours of both n and k n. It is further demonstrated that orthogonal-access schemes, which are optimal when all users are active with probability one, can be strictly suboptimal in general.

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Improved bounds on Gaussian MAC and sparse regression via Gaussian inequalities

Cited by 57 publications

References 22 publications

Gaussian Multiple and Random Access in the Finite Blocklength Regime

Gaussian Multiple and Random Access in the Finite Blocklength Regime

Massive Access for Future Wireless Communication Systems

Capacity per Unit-Energy of Gaussian Random Many-Access Channels

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