arXiv subjects

Mari Kobayashi

Publications and source records attributed to Mari Kobayashi.

At least 19 recordsLinked to original sources

arXiv2022

Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation

Motivated by automotive applications, we consider joint radar sensing and data communication for a system operating at millimeter wave (mmWave) frequency bands, where a Base Station (BS) is equipped with a co-located radar receiver and sends data using the Orthogonal Time Frequency Space (OTFS) modulation format. We consider two distinct modes of operation. In Discovery mode, a single common data stream is broadcast over a wide angular sector. The radar receiver must detect the presence of not yet acquired targets and perform coarse estimation of their parameters (angle of arrival, range, and velocity). In Tracking mode, the BS transmits multiple individual data streams to already acquired users via beamforming, while the radar receiver performs accurate estimation of the aforementioned parameters. Due to hardware complexity and power consumption constraints, we consider a hybrid digital-analog architecture where the number of RF chains and A/D converters is significantly smaller than the number of antenna array elements. In this case, a direct application of the conventional MIMO radar approach is not possible. Consequently, we advocate a beam-space approach where the vector observation at the radar receiver is obtained through a RF-domain beamforming matrix operating the dimensionality reduction from antennas to RF chains. Under this setup, we propose a likelihood function-based scheme to perform joint target detection and parameter estimation in Discovery, and high-resolution parameter estimation in Tracking mode, respectively. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while closely approaching the Cramer-Rao Lower Bound (CRLB) of the corresponding parameter estimation problem.

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arXiv2022

An Information-Theoretic Approach to Joint Sensing and Communication

A communication setup is considered where a transmitter wishes to convey a message to a receiver and simultaneously estimates the state of that receiver through a common waveform. The state is estimated at the transmitter by means of generalized feedback, i.e., a strictly causal channel output, and the known waveform. The scenario at hand is motivated by joint radar and communication, which aims to co-design radar sensing and communication over a shared spectrum and hardware. For the case of memoryless single receiver channels with i.i.d. time-varying state sequences, we fully characterize the capacity-distortion tradeoff, defined as the largest achievable rate below which a message can be conveyed reliably while satisfying some distortion constraints on state sensing. We propose a numerical method to compute the optimal input that achieves the capacity-distortion tradeoff. Then, we address memoryless state-dependent broadcast channels (BCs). For physically degraded BCs with i.i.d. time-varying state sequences, we characterize the capacity-distortion tradeoff region as a rather straightforward extension of single receiver channels. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when this capacity-distortion region is equal to the product of the capacity region and the set of achievable distortions. A number of illustrative examples demonstrate that the optimal co-design schemes outperform conventional schemes that split the resources between sensing and communication.

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arXiv2022

Coding for Sensing: An Improved Scheme for Integrated Sensing and Communication over MACs

A memoryless state-dependent multiple-access channel (MAC) is considered, where two transmitters wish to convey their messages to a single receiver while simultaneously sensing (estimating) the respective states via generalized feedbacks. For this channel, an improved inner bound is provided on the fundamental rate-distortions tradeoff which characterizes the communication rates the transmitters can achieve while simultaneously ensuring that their state-estimates satisfy desired distortion criteria. The new inner bound is based on a scheme where each transmitter codes over the generalized feedback so as to improve the state estimation at the other transmitter. This is in contrast to the schemes proposed for point-to-point and broadcast channels where coding is used only for the transmission of messages and the optimal estimators operate on a symbol-by-symbol basis on the sequences of channel inputs and feedback outputs.

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arXiv2022

Simultaneous Communication and Tracking in Arbitrary Trajectories via Beam-Space Processing

In this paper, we develop a beam tracking scheme for an orthogonal frequency division multiplexing (OFDM) Integrated Sensing and Communication (ISAC) system with a hybrid digital analog (HDA) architecture operating in the millimeter wave (mmWave) band. Our tracking method consists of an estimation step inspired by radar signal processing techniques, and a prediction step based on simple kinematic equations. The hybrid architecture exploits the predicted state information to focus only on the directions of interest, trading off beamforming gain, hardware complexity and multistream processing capabilities. Our extensive simulations in arbitrary trajectories show that the proposed method can outperform state of the art beam tracking methods in terms of prediction accuracy and consequently achievable communication rate, and is fully capable of dealing with highly non-linear dynamic motion patterns.

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arXiv2022

On the Optimal Memory-Load Tradeoff of Coded Caching for Location-Based Content

Caching at the wireless edge nodes is a promising way to boost the spatial and spectral efficiency, for the sake of alleviating networks from content-related traffic. Coded caching originally introduced by Maddah-Ali and Niesen significantly speeds up communication efficiency by transmitting multicast messages simultaneously useful to multiple users. Most prior works on coded caching are based on the assumption that each user may request all content in the library. However, in many applications the users are interested only in a limited set of content that depends on their location. Motivated by these considerations, this paper formulates the coded caching problem for location-based content with edge cache nodes. The considered problem includes a content server with access to $N$ location-based files (e.g., High-Definition maps), $K$ edge cache nodes located at different regions, and $K$ users (i.e., vehicles) each of which is in the serving region of one cache node and can retrieve the cached content of this cache node with negligible cost. Depending on the location, each user only requests a file from a location-dependent subset of the library. The objective is to minimize the worst-case load. For this novel coded caching problem, we propose a highly non-trivial converse bound under uncoded cache placement, which shows that a simple achievable scheme is optimal under uncoded cache placement. In addition, this achievable scheme is also proved to be generally order optimal within a factor of $3$. Finally, we extend the coded caching problem for location-based content to the multiaccess coded caching topology originally proposed by Hachem et al., where each user is connected to $L$ nearest cache nodes. When $L \geq 2$, we characterize the exact optimality on the worst-case load.

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arXiv2021

Beam Refinement and User State Acquisition via Integrated Sensing and Communication with OFDM

The performance of millimeter wave (mmWave) communications strongly relies on accurate beamforming both at base station and user terminal sides, referred to as beam alignment (BA). Existing BA algorithms provide initial yet coarse angle estimates as they typically use a codebook of a finite number of discreteized beams (angles). Towards emerging applications requiring timely and precise tracking of dynamically changing state of users, we consider a system where a base station with a co-located radar receiver estimates relevant state parameters of users and simultaneously sends OFDM-modulated data symbols. In particular, based on a hybrid digital analog data transmitter/radar receiver architecture, we propose a simple beam refinement and initial state acquisition scheme that can be used for beam and user location tracking in a dynamic environment. Numerical results inspired by IEEE802.11ad parameters demonstrate that the proposed method is able to improve significantly the communication rate and further achieve accurate state estimation.

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arXiv2021

Adaptive Beam Tracking based on Recurrent Neural Networks for mmWave Channels

The performance of millimeter wave (mmWave) communications critically depends on the accuracy of beamforming both at base station (BS) and user terminals (UEs) due to high isotropic path-loss and channel attenuation. In high mobility environments, accurate beam alignment becomes even more challenging as the angles of the BS and each UE must be tracked reliably and continuously. In this work, focusing on the beamforming at the BS, we propose an adaptive method based on Recurrent Neural Networks (RNN) that tracks and predicts the Angle of Departure (AoD) of a given UE. Moreover, we propose a modified frame structure to reduce beam alignment overhead and hence increase the communication rate. Our numerical experiments in a highly non-linear mobility scenario show that our proposed method is able to track the AoD accurately and achieve higher communication rate compared to more traditional methods such as the particle filter.

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arXiv2021

Cooperative Multiple-Access Channels with Distributed State Information

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e. where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e. optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

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arXiv2021

Scalable Vector Gaussian Information Bottleneck

In the context of statistical learning, the Information Bottleneck method seeks a right balance between accuracy and generalization capability through a suitable tradeoff between compression complexity, measured by minimum description length, and distortion evaluated under logarithmic loss measure. In this paper, we study a variation of the problem, called scalable information bottleneck, in which the encoder outputs multiple descriptions of the observation with increasingly richer features. The model, which is of successive-refinement type with degraded side information streams at the decoders, is motivated by some application scenarios that require varying levels of accuracy depending on the allowed (or targeted) level of complexity. We establish an analytic characterization of the optimal relevance-complexity region for vector Gaussian sources. Then, we derive a variational inference type algorithm for general sources with unknown distribution; and show means of parametrizing it using neural networks. Finally, we provide experimental results on the MNIST dataset which illustrate that the proposed method generalizes better to unseen data during the training phase.

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arXiv2021

D2D-Aided Multi-Antenna Multicasting under Generalized CSIT

Multicasting, where a base station (BS) wishes to convey the same message to several user equipments (UEs), represents a common yet highly challenging wireless scenario. In fact, guaranteeing decodability by the whole UE population proves to be a major performance bottleneck since the UEs in poor channel conditions ultimately determine the achievable rate. To overcome this issue, two-phase cooperative multicasting schemes, which use conventional multicasting in a first phase and leverage device-to-device (D2D) communications in a second phase to effectively spread the message, have been extensively studied. However, most works are limited either to the simple case of single-antenna BS or to a specific channel state information at the transmitter (CSIT) setup. This paper proposes a general two-phase framework that is applicable to the cases of perfect, statistical, and topological CSIT in the presence of multiple antennas at the BS. The proposed method exploits the precoding capabilities at the BS, which enable targeting specific UEs that can effectively serve as D2D relays towards the remaining UEs, and maximize the multicast rate under some outage constraint. Numerical results show that our schemes bring substantial gains over traditional single-phase multicasting and overcome the worst-UE bottleneck behavior in all the considered CSIT configurations.

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arXiv2020

Joint Sensing and Communication over Memoryless Broadcast Channels

A memoryless state-dependent broadcast channel (BC) is considered, where the transmitter wishes to convey two private messages to two receivers while simultaneously estimating the respective states via generalized feedback. The model at hand is motivated by a joint radar and communication system where radar and data applications share the same frequency band. For physically degraded BCs with i.i.d. state sequences, we characterize the capacity-distortion tradeoff region. For general BCs, we provide inner and outer bounds on the capacitydistortion region, as well as a sufficient condition when it is equal to the product of the capacity region and the set of achievable distortion. Interestingly, the proposed synergetic design significantly outperforms a conventional approach that splits the resource either for sensing or communication.

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arXiv2020

On the Relevance-Complexity Region of Scalable Information Bottleneck

The Information Bottleneck method is a learning technique that seeks a right balance between accuracy and generalization capability through a suitable tradeoff between compression complexity, measured by minimum description length, and distortion evaluated under logarithmic loss measure. In this paper, we study a variation of the problem, called scalable information bottleneck, where the encoder outputs multiple descriptions of the observation with increasingly richer features. The problem at hand is motivated by some application scenarios that require varying levels of accuracy depending on the allowed level of generalization. First, we establish explicit (analytic) characterizations of the relevance-complexity region for memoryless Gaussian sources and memoryless binary sources. Then, we derive a Blahut-Arimoto type algorithm that allows us to compute (an approximation of) the region for general discrete sources. Finally, an application example in the pattern classification problem is provided along with numerical results.

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arXiv2020

Hybrid Digital-Analog Beamforming and MIMO Radar with OTFS Modulation

Motivated by future automotive applications, we study some joint radar target detection and parameter estimation problems where the transmitter, equipped with a mono-static MIMO radar, wishes to detect multiple targets and then estimate their respective parameters, while simultaneously communicating information data using orthogonal time frequency space (OTFS) modulation. Assuming that the number of radio frequency chains is smaller than the number of antennas over the mmWave frequency band, we design hybrid digital-analog beamforming at the radar transmitter adapted to different operating phases. The first scenario considers a wide angular beam in order to perform the target detection and parameter estimation, while multicasting a common message to all possible active users. The second scenario considers narrow angular beams to send information streams individually to the already detected users and simultaneously keep tracking of their respective parameters. Under this setup, we propose an efficient maximum likelihood scheme combined with hybrid beamforming to jointly perform target detection and parameter estimation. Our numerical results demonstrate that the proposed algorithm is able to reliably detect multiple targets with a sufficient number of antennas and achieves the Cramér-Rao lower bound for radar parameter estimation such as delay, Doppler and angle-of-arrival (AoA).

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arXiv2020

Scalable Content Delivery with Coded Caching in Multi-Antenna Fading Channels

We consider the content delivery problem in a fading multi-input single-output channel with cache-aided users. We are interested in the scalability of the equivalent content delivery rate when the number of users, $K$, is large. Analytical results show that, using coded caching and wireless multicasting, without channel state information at the transmitter (CSIT), linear scaling of the content delivery rate with respect to $K$ can be achieved in some different ways. First, if the multicast transmission spans over $L$ independent sub-channels, e.g., in quasi-static fading if $L = 1$, and in block fading or multi-carrier systems if $L>1$, linear scaling can be obtained when the product of the number of transmit antennas and the number of sub-channels scales logarithmically with $K$. Second, even with a fixed number of antennas, we can achieve the linear scaling with a threshold-based user selection requiring only one-bit feedbacks from the users. When CSIT is available, we propose a mixed strategy that combines spatial multiplexing and multicasting. Numerical results show that, by optimizing the power split between spatial multiplexing and multicasting, we can achieve a significant gain of the content delivery rate with moderate cache size.

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arXiv2020

Joint Radar Target Detection and Parameter Estimation with MIMO OTFS

Motivated by future automotive applications, we study the joint target detection and parameter estimation problem using orthogonal time frequency space (OTFS), a digital modulation format robust to time-frequency selective channels. Assuming the transmitter is equipped with a mono-static MIMO radar, we propose an efficient maximum likelihood based approach to detect targets and estimate the corresponding delay, Doppler, and angle-of-arrival parameters. In order to reduce the computational complexity associated to the high-dimensional search, our scheme proceeds in two steps, i.e., target detection and coarse parameter estimation followed by refined parameter estimation. Interestingly, our numerical results demonstrate that the proposed scheme is able to identify multiple targets if they are separated in at least one domain out of three (delay, Doppler, and angle), while achieving the Cramér-Rao lower bound for the parameter estimation.

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arXiv2020

Adaptive Coded Caching for Fair Delivery over Fading Channels

The performance of existing coded caching schemes is sensitive to the worst channel quality, a problem which is exacerbated when communicating over fading channels. In this paper, we address this limitation in the following manner: in short-term, we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness among users. Our online scheme combines (i) the classical decentralized coded caching scheme \cite{maddah2013decentralized} with (ii) joint scheduling and power control for the fading broadcast channel, as well as (iii) congestion control for ensuring the optimal long-term average performance. We prove that our online delivery scheme maximizes the alpha-fair utility among all schemes restricted to decentralized placement. By tuning the value of alpha, the proposed scheme can achieve different operating points on the average delivery rate region and tune performance according to an operator's choice. We demonstrate via simulations that our scheme outperforms two baseline schemes: (a) standard coded caching with multicast transmission, limited by the worst channel user yet exploiting the global caching gain; (b) opportunistic scheduling with unicast transmissions exploiting the fading diversity but limited to local caching gain.

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arXiv2019

On the Effectiveness of OTFS for Joint Radar and Communication

We consider a joint radar estimation and communication system using orthogonal time frequency space (OTFS) modulation. The scenario is motivated by vehicular applications where a vehicle equipped with a mono-static radar wishes to communicate data to its target receiver, while estimating parameters of interest related to this receiver. In a point-to-point communication setting over multi-path time-frequency selective channels, we study the joint radar and communication system from two perspectives, i.e., the radar estimation at the transmitter as well as the symbol detection at the receiver. For the radar estimation part, we derive an efficient approximated Maximum Likelihood algorithm and the corresponding Cramér- Rao lower bound for range and velocity estimation. Numerical examples demonstrate that multi-carrier digital formats such as OTFS can achieve as accurate radar estimation as state-of-the-art radar waveforms such as frequency-modulated continuous wave (FMCW). For the data detection part, we focus on separate detection and decoding and consider a soft-output detector that exploits efficiently the channel sparsity in the Doppler-delay domain. We quantify the detector performance in terms of its pragmatic capacity, i.e. the achievable rate of the channel induced by the signal constellation and the detector soft output. Simulations show that the proposed scheme outperforms concurrent state-of-the-art solutions. Overall, our work shows that a suitable digitally modulated waveform enables to efficiently operate joint radar and communication by achieving full information rate of the modulation and near-optimal radar estimation performance. Furthermore, OTFS appears to be particularly suited to the scope.

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arXiv2019

D2D-Aided Multi-Antenna Multicasting

Multicast services, whereby a common valuable message needs to reach a whole population of user equipments (UEs), are gaining attention on account of new applications such as vehicular networks. As it proves challenging to guarantee decodability by every UE in a large population, service reliability is indeed the Achilles' heel of multicast transmissions. To circumvent this problem, a two-phase protocol capitalizing on device-to-device (D2D) links between UEs has been proposed, which overcomes the vanishing behavior of the multicast rate. In this paper, we revisit such D2D-aided protocol in the new light of precoding capabilities at the base station (BS). We obtain an enhanced scheme that aims at selecting a subset of UEs who cooperate to spread the common message across the rest of the network via D2D retransmissions. With the objective of maximizing the multicast rate under some outage constraint, we propose an algorithm with provable convergence that jointly identifies the most pertinent relaying UEs and optimizes the precoding strategy at the BS.

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