15 research outputs found
Harnessing Bursty Interference in Multicarrier Systems with Feedback
We study parallel symmetric 2-user interference channels when the
interference is bursty and feedback is available from the respective receivers.
Presence of interference in each subcarrier is modeled as a memoryless
Bernoulli random state. The states across subcarriers are drawn from an
arbitrary joint distribution with the same marginal probability for each
subcarrier and instantiated i.i.d. over time. For the linear deterministic
setup, we give a complete characterization of the capacity region. For the
setup with Gaussian noise, we give outer bounds and a tight generalized degrees
of freedom characterization. We propose a novel helping mechanism which enables
subcarriers in very strong interference regime to help in recovering interfered
signals for subcarriers in strong and weak interference regimes. Depending on
the interference and burstiness regime, the inner bounds either employ the
proposed helping mechanism to code across subcarriers or treat the subcarriers
separately. The outer bounds demonstrate a connection to a subset entropy
inequality by Madiman and Tetali.Comment: A shorter version of this work will appear in the proceedings of ISIT
201
Robust Signaling for Bursty Interference
This paper studies a bursty interference channel, where the presence/absence
of interference is modeled by a block-i.i.d.\ Bernoulli process that stays
constant for a duration of symbols (referred to as coherence block) and
then changes independently to a new state. We consider both a quasi-static
setup, where the interference state remains constant during the whole
transmission of the codeword, and an ergodic setup, where a codeword spans
several coherence blocks. For the quasi-static setup, we study the largest rate
of a coding strategy that provides reliable communication at a basic rate and
allows an increased (opportunistic) rate when there is no interference. For the
ergodic setup, we study the largest achievable rate. We study how non-causal
knowledge of the interference state, referred to as channel-state information
(CSI), affects the achievable rates. We derive converse and achievability
bounds for (i) local CSI at the receiver-side only; (ii) local CSI at the
transmitter- and receiver-side, and (iii) global CSI at all nodes. Our bounds
allow us to identify when interference burstiness is beneficial and in which
scenarios global CSI outperforms local CSI. The joint treatment of the
quasi-static and ergodic setup further allows for a thorough comparison of
these two setups.Comment: 67 pages, 39 figure
Center for Aeronautics and Space Information Sciences
This report summarizes the research done during 1991/92 under the Center for Aeronautics and Space Information Science (CASIS) program. The topics covered are computer architecture, networking, and neural nets
Capacity limits of bursty interference channels
MenciĂłn Internacional en el tĂtulo de doctorThis dissertation studies the effects of interference burstiness in the transmission
of data in wireless networks. In particular, we investigate the effects of
this phenomenon on the largest data rate at which one can communicate with a
vanishing small probability of error, i.e., on channel capacity. Specifically, we study
the capacity of two different channel models as described in the next sections.
Linear deterministic bursty interference channel.
First, we consider a two-user linear deterministic bursty interference channel (IC),
where the presence or absence of interference is modeled by a block- independent
and identically distributed (IID) Bernoulli process that stays constant for a
duration of T consecutive symbols (this is sometimes referred to as a coherence
block) and then changes independently to a new interference state. We assume
that the channel coefficients of the communication and interference links remain
constant during the whole message transmission. For this channel, we consider
both its quasi-static setup where the interference state remains constant during
the whole transmission of the codeword (which corresponds to the case whether
the blocklength N is smaller than T) and its ergodic setup where a codeword
spans several coherence blocks. For the quasi-static setup, we follow the seminal
works by Khude, Prabhakaran and Viswanath and study the largest sum rate of
a coding strategy that provides reliable communication at a basic (or worstcase)
rate R and allows an increased (opportunistic) rate ΔR in absence of interference.
For the ergodic scenario, we study the largest achievable sum rate as commonly
considered in the multi-user information theory literature. We study how (noncausal)
knowledge of the interference state, referred to as channel state information
(CSI), affects the sum capacity. Specifically, for both scenarios, we derive converse
and achievability bounds on the sum capacity for (i) local CSI at the receiverside
only; (ii) when each transmitter and receiver has local CSI, and (iii) global CSI
at all nodes, assuming both that interference states are independent of each other
and that they are fully correlated. Our bounds allow us to identify regions and
conditions where interference burstiness is beneficial and in which scenarios global CSI improves upon local CSI. Specifically, we show the following:
• Exploiting burstiness: For the quasi-static scenario we have shown that
in presence of local CSI, burstiness is only beneficial if the interference
region is very weak or weak. In contrast, for global CSI, burstiness is
beneficial for all interference regions, except the very strong interference
region, where the sum capacity corresponds to that of two parallel channels
without interference. For the ergodic scenario, we have shown that, under
global CSI, burstiness is beneficial for all interference regions and all possible
values of p. For local CSI at the receiver-side only, burstiness is beneficial for
all values of p and for very weak and weak interference regions. However, for
moderate and strong interference regions, burstiness is only of clear benefit
if the interference is present at most half of the time.
• Exploiting CSI: For the quasi-static scenario, local CSI at the transmitter is
not beneficial. This is in stark contrast to the ergodic scenario, where local
CSI at the transmitter-side is beneficial. Intuitively, in the ergodic scenario
the input distributions depend on the realizations of the interference states.
Hence, adapting the input distributions to these realizations increases the
sum capacity. In contrast, in the quasi-static case, the worst-case scenario
(presence of interference) and the best-case scenario (absence of interference)
are treated separately. Hence, there is no difference to the case of having
local CSI only at the receiver side. Featuring global CSI at all nodes yields
an increased sum rate for both the quasi-static and the ergodic scenarios.
The joint treatment of the quasi-static and the ergodic scenarios allows us to
thoroughly compare the sum capacities of these two scenarios. While the converse
bounds for the quasi-static scenario and local CSI at the receiver-side appeared
before in the literature, we present a novel proof based on an information density
approach and the Verd´u-Han lemma. This approach does not only allow for
rigorous yet clear proofs, it also enables more refined analyses of the probabilities
of error that worst-case and opportunistic messages can be decoded correctly.
For the converse bounds in the ergodic scenario, we use Fano’s inequality as the
standard approach to derive converse bounds in the multi-user information theory literature.
Bursty noncoherent wireless networks.
The linear deterministic model can be viewed as a rough approximation of a
fading channel, which has additive and multiplicative noise. The multiplicative
noise is referred to as fading. As we have seen in the previous section, the linear
deterministic model provides a rough understanding of the effects of interference
burstiness on the capacity of the two-user IC. Now, we extend our analysis to a
wireless network with a very large number of users and we do not approximate
the fading channel by a linear deterministic model. That is, we consider a memoryless
flat-fading channel with an infinite number of interferers. We incorporate
interference burstiness by an IID Bernoulli process that stays constant during the
whole transmission of the codeword.
The channel capacity of wireless networks is often studied under the assumption
that the communicating nodes have perfect knowledge of the fading coefficients in
the network. However, it is prima-facie unclear whether this perfect knowledge
of the channel coefficients can actually be obtained in practical systems. For
this reason, we study in this dissertation the channel capacity of a noncoherent
model where the nodes do not have perfect knowledge of the fading coefficients.
More precisely, we assume that the nodes know only the statistics of the channel
coefficients but not their realizations. We further assume that the interference
state (modeling interference burstiness) is known non-causally at the receiver-side
only. To the best of our knowledge, one of the few works that studies the capacity
of noncoherent wireless networks (without considering interference burstiness)
is by Lozano, Heath, and Andrews. Inter alia, Lozano et al. show that in the
absence of perfect knowledge of the channel coefficients, if the channel inputs
are given by the square-root of the transmit power times a power-independent
random variable, and if interference is always present (hence, it is non-bursty),
then the achievable information rate is bounded in the signal-to-noise ratio (SNR).
However, the considered inputs do not necessarily achieve capacity, so one may
argue that the information rate is bounded in the SNR because of the suboptimal
input distribution. Therefore, in our analysis, we allow the input distribution
to change arbitrarily with the SNR. We analyze the asymptotic behavior of the
channel capacity in the limit as the SNR tends to infinity. We assume that all
nodes (transmitting and interfering) use the same codebook. This implies that
each node is transmitting at the same rate, while at the same time it keeps the analysis tractable. We demonstrate that if the nodes do not cooperate and if the
variances of the path gains decay exponentially or slower, then the achievable
information rate remains bounded in the SNR, even if the input distribution
is allowed to change arbitrarily with the transmit power, irrespective of the
interference burstiness. Specifically, for this channel, we show the following:
• The channel capacity is bounded in the SNR. This suggests that noncoherent
wireless networks are extremely power inefficient at high SNR.
• Our bound further shows that interference burstiness does not change the
behavior of channel capacity. While our upper bound on the channel capacity
grows as the channel becomes more bursty, it remains bounded in the SNR.
Thus, interference burstiness cannot be exploited to mitigate the power
inefficiency at high SNR.
Possible strategies that could mitigate the power inefficiency of noncoherent
wireless networks and that have not been explored in this thesis are cooperation
between users and improved channel estimation strategies. Indeed,
coherent wireless networks, in which users have perfect knowledge of the
fading coefficients, have a capacity that grows to infinity with the SNR.
Furthermore, for such networks, the most efficient transmission strategies,
such as interference alignment, rely on cooperation. Our results suggest that
these two strategies may be essential to obtain an unbounded capacity in the SNR.Programa Oficial de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Ignacio SantamarĂa Caballero.- Secretario: David RamĂrez GarcĂa, David.- Vocal: Paul de Kerre
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges
It is anticipated that integrated sensing and communications (ISAC) would be
one of the key enablers of next-generation wireless networks (such as beyond 5G
(B5G) and 6G) for supporting a variety of emerging applications. In this paper,
we provide a comprehensive review of the recent advances in ISAC systems, with
a particular focus on their foundations, system design, networking aspects and
ISAC applications. Furthermore, we discuss the corresponding open questions of
the above that emerged in each issue. Hence, we commence with the information
theory of sensing and communications (SC), followed by the
information-theoretic limits of ISAC systems by shedding light on the
fundamental performance metrics. Next, we discuss their clock synchronization
and phase offset problems, the associated Pareto-optimal signaling strategies,
as well as the associated super-resolution ISAC system design. Moreover, we
envision that ISAC ushers in a paradigm shift for the future cellular networks
relying on network sensing, transforming the classic cellular architecture,
cross-layer resource management methods, and transmission protocols. In ISAC
applications, we further highlight the security and privacy issues of wireless
sensing. Finally, we close by studying the recent advances in a representative
ISAC use case, namely the multi-object multi-task (MOMT) recognition problem
using wireless signals.Comment: 26 pages, 22 figures, resubmitted to IEEE Journal. Appreciation for
the outstanding contributions of coauthors in the paper
Deep learning for wireless communications : flexible architectures and multitask learning
Demand for wireless connectivity has never been higher and continues to grow rapidly.
Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled
breathroughs in radio configurability, enabling dynamic spectrum access and physical
layer optimizations at runtime. In recent years Machine Learning (ML) has been a
key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML.Demand for wireless connectivity has never been higher and continues to grow rapidly.
Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled
breathroughs in radio configurability, enabling dynamic spectrum access and physical
layer optimizations at runtime. In recent years Machine Learning (ML) has been a
key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML
Enhancing spectrum utilization through cooperation and cognition in wireless systems
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections."February 2013." Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 201-217).We have seen a proliferation of wireless technologies and devices in recent years. The resulting explosion of wireless demand has put immense pressure on available spectrum. Improving spectrum utilization is therefore necessary to enable wireless networks to keep up with burgeoning demand. This dissertation presents a cognitive and cooperative wireless architecture that significantly enhances spectrum utilization. Specifically, it introduces four new systems that embody a cross-layer design for cognition and cooperation. The first system, SWIFT, is a cognitive cross technology solution that enables wideband devices to exploit higher layer network semantics to adaptively sense which portions of the spectrum are occupied by unknown narrowband devices, and weave the remaining unoccupied spectrum bands into a single high-throughput wideband link. Second, FARA is a cooperative system that enables multi-channel wireless solutions like 802.11 to dynamically use all available channels for all devices in a performance-aware manner by using information from the physical layer and allocating to each link the frequency bands that show the highest performance for that link. SourceSync, the third system, enables wireless nodes in last-hop and wireless mesh networks to cooperatively transmit synchronously in order to exploit channel diversity and increase reliability. Finally, MegaMIMO enables wireless throughput to scale linearly with the number of transmitters by enabling multiple wireless transmitters to transmit simultaneously in the same frequency bands to multiple wireless receivers without interfering with each other. The systems in this dissertation demonstrate the practicality of cognitive and cooperative wireless systems to enable spectrum sharing. Further, as part of these systems, we design several novel primitives - adaptive spectrum sensing, time alignment, frequency synchronization, and distributed phase-coherent transmission, that can serve as fundamental building blocks for wireless cognition and cooperation. Finally, we have implemented all four systems described in this dissertation, and evaluated them in wireless testbeds, demonstrating large gains in practice.by Hariharan Shankar Rahul.Ph.D
Méthodes de codage et d'estimation adaptative appliquées aux communications sans fil
Les recherches et les contributions présentées portent sur des techniques de traitement du signal appliquées aux communications sans fil. Elles s’articulent autour des points suivants : (1) l’estimation adaptative de canaux de communication dans différents contextes applicatifs, (2) la correction de bruit impulsionnel et la réduction du niveau de PAPR (Peak to Average Power Ratio) dans un système multi-porteuse, (3) l’optimisation de schémas de transmission pour la diffusion sur des canaux gaussiens avec/sans contrainte de sécurité, (4) l’analyse, l’interprétation et l’amélioration des algorithmes de décodage itératif par le biais de l’optimisation, de la théorie des jeux et des outils statistiques. L’accent est plus particulièrement mis sur le dernier thème