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Giuseppe Caire

Publications and source records attributed to Giuseppe Caire.

At least 181 records · Page 10Linked to original sources
arXiv2023

Extended Target Parameter Estimation and Tracking with an HDA Setup for ISAC Applications

We investigate radar parameter estimation and beam tracking with a hybrid digital-analog (HDA) architecture in a multi-block measurement framework using an extended target model. In the considered setup, the backscattered data signal is utilized to predict the user position in the next time slots. Specifically, a simplified maximum likelihood framework is adopted for parameter estimation, based on which a simple tracking scheme is also developed. Furthermore, the proposed framework supports adaptive transmitter beamwidth selection, whose effects on the communication performance are also studied. Finally, we verify the effectiveness of the proposed framework via numerical simulations over complex motion patterns that emulate a realistic integrated sensing and communication (ISAC) scenario.

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arXiv2023

Integrated Sensing and Communication with MOCZ Waveform

In this work, we propose a waveform based on Modulation on Conjugate-reciprocal Zeros (MOCZ) originally proposed for short-packet communications in [1], as a new Integrated Sensing and Communication (ISAC) waveform. Having previously established the key advantages of MOCZ for noncoherent and sporadic communication, here we leverage the optimal auto-correlation property of Binary MOCZ (BMOCZ) for sensing applications. Due to this property, which eliminates the need for separate communication and radar-centric waveforms, we propose a new frame structure for ISAC, where pilot sequences and preambles become obsolete and are completely removed from the frame. As a result, the data rate can be significantly improved. Aimed at (hardware-) cost-effective radar-sensing applications, we consider a Hybrid Digital-Analog (HDA) beamforming architecture for data transmission and radar sensing. We demonstrate via extensive simulations, that a communication data rate, significantly higher than existing standards can be achieved, while simultaneously achieving sensing performance comparable to state-of-the-art sensing systems.

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arXiv2023

Reduced-Complexity Cross-Domain Iterative Detection for OTFS Modulation via Delay-Doppler Decoupling

In this paper, a reduced-complexity cross-domain iterative detection for orthogonal time frequency space (OTFS) modulation is proposed, which exploits channel properties in both time and delay-Doppler domains. Specifically, we first show that in the time domain effective channel, the path delay only introduces interference among samples in adjacent time slots, while the Doppler becomes a phase term that does not affect the channel sparsity. This ``band-limited'' matrix structure motivates us to apply a reduced-size linear minimum mean square error (LMMSE) filter to eliminate the effect of delay in the time domain, while exploiting the cross-domain iteration for minimizing the effect of Doppler by noticing that the time and Doppler are a pair of Fourier dual. The state (MSE) evolution was derived and compared with bounds to verify the effectiveness of the proposed scheme. Simulation results demonstrate that the proposed scheme achieves almost the same error performance as the optimal detection, but only requires a reduced complexity.

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arXiv2023

MAC Wiretap Channels with Confidential and Open Messages: Improved Achievable Region and Low-complexity Precoder Design

This paper investigates the achievable region and precoder design for multiple access wiretap (MAC-WT) channels, where each user transmits both secret and open (i.e., non-confidential) messages. All these messages are intended for the legitimate receiver (or Bob for brevity) and the eavesdropper (Eve) is interested only in the secret messages of all users. By allowing users with zero secret message rate to act as conventional MAC channel users with no wiretapping, we show that the achievable region of the discrete memoryless (DM) MAC-WT channel given in [1] can be enlarged. In [1], the achievability was proven by considering the two-user case, making it possible to prove a key auxiliary lemma by directly using the Fourier-Motzkin elimination procedure. However, this approach does not generalize to the case with any number of users. In this paper, we provide a new region that generally enlarges that in [1] and provide general achievability proof.

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arXiv2023

On the Fundamental Tradeoff of Integrated Sensing and Communications Under Gaussian Channels

ISAC is recognized as a promising technology for the next-generation wireless networks, which provides significant performance gains over individual S&C systems via the shared use of wireless resources. The characterization of the S&C performance tradeoff is at the core of the theoretical foundation of ISAC. In this paper, we consider a point-to-point ISAC model under vector Gaussian channels, and propose to use the CRB-rate region as a basic tool for depicting the fundamental S&C tradeoff. In particular, we consider the scenario where a unified ISAC waveform is emitted from a dual-functional ISAC Tx, which simultaneously performs S&C tasks with a communication Rx and a sensing Rx. In order to perform both S&C tasks, the ISAC waveform is required to be random to convey communication information, with realizations being perfectly known at both the ISAC Tx and the sensing Rx as a reference sensing signal as in typical radar systems. As the main contribution of this paper, we characterize the S&C performance at the two corner points of the CRB-rate region, namely, $P_{SC}$ indicating the max. achievable rate constrained by the min. CRB, and $P_{CS}$ indicating the min. achievable CRB constrained by the max. rate. In particular, we derive the high-SNR capacity at $P_{SC}$, and provide lower and upper bounds for the sensing CRB at $P_{CS}$. We show that these two points can be achieved by the conventional Gaussian signaling and a novel strategy relying on the uniform distribution over the Stiefel manifold, respectively. Based on the above-mentioned analysis, we provide an outer bound and various inner bounds for the achievable CRB-rate regions. Our main results reveal a two-fold tradeoff in ISAC systems, consisting of the subspace tradeoff (ST) and the deterministic-random tradeoff (DRT) that depend on the resource allocation and data modulation schemes employed for S&C, respectively.

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arXiv2023

Optimal Fairness Scheduling for Coded Caching in Multi-AP Wireless Local Area Networks

Coded caching (CC) schemes exploit the cumulative cache memory of the users and simple linear coding to turn unicast traffic (individual file requests) into a multicast transmission. For the originally proposed $K$-user single-server/single shared link network model, CC yields an $O(K)$ gain with respect to conventional uncoded caching with the same per-user memory. While several information-theoretic optimality results for a variety of problems and carefully crafted network topologies have been proved, the gains and suitability of CC for practical scenarios such as content streaming over existing wireless networks have not yet been fully demonstrated. In this work, we consider CC for on-demand video streaming over WLANs where multiple users are served simultaneously by multiple spatially distributed access points (AP). Users sequentially request video ``chunks". The CC scheme operates above the IP layer, leaving the underlying standard physical layer and MAC layer untouched. The cache placement is completely asynchronous and decentralized, and the users are placed at random over the network coverage area. For such a system, we consider the region of achievable long-term average delivery rate (defined as the number of video chunks delivered per unit of time) and study the per-user rate distribution under proportional fairness scheduling. We also consider reduced complexity scheduling strategies and compare them with standard state-of-the-art techniques such as conventional (uncoded) caching and collision avoidance by allocating APs on different sub-channels (i.e., frequency reuse).

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arXiv2023

User-Centric Clustering Under Fairness Scheduling in Cell-Free Massive MIMO

We consider fairness scheduling in a user-centric cell-free massive MIMO network, where $L$ remote radio units, each with $M$ antennas, serve $K_{\rm tot} \approx LM$ user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where $K_{\rm act} \approx \frac{LM}{2}$ UEs are simultaneously active in any given time-frequency slots. However, the number of users $K_{\rm tot}$ in the network is usually much larger. This requires that users are scheduled over the time-frequency resource and achieve a certain throughput rate as an average over the slots. We impose throughput fairness among UEs with a scheduling approach aiming to maximize a concave component-wise non-decreasing network utility function of the per-user throughput rates. In cell-free user-centric networks, the pilot and cluster assignment is usually done for a given set of active users. Combined with fairness scheduling, this requires pilot and cluster reassignment at each scheduling slot, involving an enormous overhead of control signaling exchange between network entities. We propose a fixed pilot and cluster assignment scheme (independent of the scheduling decisions), which outperforms the baseline method in terms of UE throughput, while requiring much less control information exchange between network entities.

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arXiv2023

Practical Channel Splicing using OFDM Waveforms for Joint Communication and Sensing in the IoT

Channel splicing is a rather new and very promising concept. It allows to realize a wideband channel sounder by combining multiple narrow-band measurements. Among others, channel splicing is a sparse sensing techniques suggested for use in joint communication and sensing (JCAS), channel measurements and prediction using cheap hardware that cannot measure wideband channels directly such as in the internet of things (IoT). This work validates the practicality of a channel splicing technique by integrating it into an OFDM-based IEEE 802.11ac system, which we consider representative for many IoT solutions. Our system allows computing both the channel impulse response (CIR) and the channel frequency response (CFR). In this paper, we concentrate on the impact of the number of sub-bands in our study and show that even using only 50% of the overall spectrum leads to very accurate CIR measures. We validate the system in simulation and confirm the results in an experimental in-door scenario using software defined radios.

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arXiv2023

Distributed Information Bottleneck for a Primitive Gaussian Diamond MIMO Channel

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

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arXiv2023

On the State Estimation Error of "Beam-Pointing'' Channels: The Binary Case

Sensing capabilities as an integral part of the network have been identified as a novel feature of sixth-generation (6G) wireless networks. As a key driver, millimeterwave (mmWave) communication largely boosts speed, capacities, and connectivity. In order to maximize the potential of mmWave communication, precise and fast beam acquisition (BA) is crucial, since it compensates for a high pathloss and provides a large beamforming gain. Practically, the angle-of-departure (AoD) remains almost constant over numerous consecutive time slots, the backscatter signal experiences some delay, and the hardware is restricted under the peak power constraint. This work captures these main features by a simple binary beam-pointing (BBP) channel model with in-block memory (iBM) [1], peak cost constraint, and one unit-delayed feedback. In particular, we focus on the sensing capabilities of such a model and characterize the performance of the BA process in terms of the Hamming distortion of the estimated channel state. We encode the position of the AoD and derive the minimum distortion of the BBP channel under the peak cost constraint with no communication constraint. Our previous work [2] proposed a joint communication and sensing (JCAS) algorithm, which achieves the capacity of the same channel model. Herein, we show that by employing this JCAS transmission strategy, optimal data communication and channel estimation can be accomplished simultaneously. This yields the complete characterization of the capacity-distortion tradeoff for this model.

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arXiv2023

Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach

Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user from the received signal strength (RSS) of a small number of Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the RSS measurements of the users to be localized, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps. The proposed method does not require generating RSS fingerprints of each specific area where the localization task is performed and is suitable for real-time applications. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and made publicly available for the research community. By using these datasets, we also provide a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario and show numerically that LocUNet outperforms all the compared methods.

cs.LG↗
arXiv2023

Beam Alignment with an Intelligent Reflecting Surface for Integrated Sensing and Communication

In a typical communication system, in order to maintain a desired SNR level, initial beam alignment (BA) must be established prior to data transmission. In a setup where a Base Station (BS) Tx sends data via a digitally modulated waveform, we propose a User Equipment (UE) enhanced with a Hybrid Intelligent Reflecting Surface (HIRS) to aid beam alignment. A novel multi-slot estimation scheme is developed that alleviates the restrictions imposed by the Hybrid Digital-Analog (HDA) architecture of the HIRS and the BS. To demonstrate the effectiveness of the proposed BA scheme, we derive the CRLB of the parameter estimation scheme and provide numerical results.

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arXiv2023

Deep-Learning Aided Channel Training and Precoding in FDD Massive MIMO with Channel Statistics Knowledge

We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to effective channels. Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from received feedback and constructs a linear precoder by concatenating the optimized pre-beamforming matrix with a zero-forcing precoder over the effective channels. We show that the proposed method yields significantly higher sum-rates than the state-of-the-art DNN-based channel training and precoding scheme, especially in scenarios with small pilot and feedback size relative to the channel coherence block length. Unlike many works in the literature, our proposition does not involve deployment of a DNN at the user side, which typically comes at a high computational cost and parameter-transmission overhead on the system, and is therefore considerably more practical.

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arXiv2023

Non-uniform array design for robust LoS MIMO via convex optimization

The array design problem of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) transmit environment is examined. As uniform array configurations at the transmitter (Tx) and receiver (Rx) are optimal at specific transmit distances only, they lead to reduced spectral efficiency over a range of transmit distances. To that end, the joint design of nonuniform Tx and Rx arrays towards maximizing the minimum capacity of a LoS MIMO system across a range of transmit distances is investigated in this paper. By introducing convex relaxation, the joint Tx and Rx array design is cast as a convex optimization problem, which is solved in a iterative manner. In addition, we also implement a local search to obtain a refined solution that achieves an improved performance. It is shown that the non-uniform configurations designed with our proposed approach outperform uniform and non-uniform array designs of the literature in terms of capacity and/or complexity.

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arXiv2023

On the Information Theoretic Secure Aggregation with Uncoded Groupwise Keys

Secure aggregation, which is a core component of federated learning, aggregates locally trained models from distributed users at a central server. The ``secure'' nature of such aggregation consists of the fact that no information about the local users' data must be leaked to the server except the aggregated local models. In order to guarantee security, some keys may be shared among the users. After the key sharing phase, each user masks its trained model which is then sent to the server. This paper follows the information theoretic secure aggregation problem originally formulated by Zhao and Sun, with the objective to characterize the minimum communication cost from the K users in the model aggregation phase. Due to user dropouts, the server may not receive all messages from the users. A secure aggregation scheme should tolerate the dropouts of at most $K-U$ users. The optimal communication cost is characterized by Zhao and Sun, but with the assumption that the keys stored by the users could be any random variables with arbitrary dependency. On the motivation that uncoded groupwise keys are more convenient to be shared and could be used in large range of applications besides federated learning, in this paper we assume the key variables are mutually independent and each key is shared by a group of S users. To the best of our knowledge, all existing secure aggregation schemes assign coded keys to the users. We show that if $S> K-U$, a new secure aggregation scheme with uncoded groupwise keys can achieve the same optimal communication cost as the best scheme with coded keys; if $S \leq K-U$, uncoded groupwise key sharing is strictly sub-optimal. Finally, we also implement our proposed secure aggregation scheme into Amazon EC2, which are then compared with the existing secure aggregation schemes with offline key sharing.

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arXiv2023

Delay-Doppler Domain Tomlinson-Harashima Precoding for OTFS-based Downlink MU-MIMO Transmissions: Linear Complexity Implementation and Scaling Law Analysis

Orthogonal time frequency space (OTFS) modulation is a recently proposed delay-Doppler (DD) domain communication scheme, which has shown promising performance in general wireless communications, especially over high-mobility channels. In this paper, we investigate DD domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output OTFS (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain interference in a symbol-by-symbol manner. We also study the achievable rate performance for the proposed scheme by leveraging the information-theoretical equivalent models. In particular, we show that the proposed scheme can achieve a near optimal performance in the high signal-to-noise ratio (SNR) regime. More importantly, scaling laws for achievable rates with respect to number of antennas and users are derived, which indicate that the achievable rate increases logarithmically with the number of antennas and linearly with the number of users. Our numerical results align well with our findings and also demonstrate a significant improvement compared to existing MU-MIMO schemes on OTFS and orthogonal frequency-division multiplexing (OFDM).

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arXiv2023

Deterministic-Random Tradeoff of Integrated Sensing and Communications in Gaussian Channels: A Rate-Distortion Perspective

Integrated sensing and communications (ISAC) is recognized as a key enabling technology for future wireless networks. To shed light on the fundamental performance limits of ISAC systems, this paper studies the deterministic-random tradeoff between sensing and communications (S&C) from a rate-distortion perspective under vector Gaussian channels. We model the ISAC signal as a random matrix that carries information, whose realization is perfectly known to the sensing receiver, but is unknown to the communication receiver. We characterize the sensing mutual information conditioned on the random ISAC signal, and show that it provides a universal lower bound for distortion metrics of sensing. Furthermore, we prove that the distortion lower bound is minimized if the sample covariance matrix of the ISAC signal is deterministic. We then offer our understanding of the main results by interpreting wireless sensing as non-cooperative source-channel coding, and reveal the deterministic-random tradeoff of S&C for ISAC systems. Finally, we provide sufficient conditions for the achievability of the distortion bound by analyzing a specific example of target response matrix estimation.

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arXiv2022

Little or no equalization is needed in energy-efficient sub-THz mobile access

By trading coverage and hardware complexity for abundance of spectrum, sub-THz mobile access networks are expected to operate under highly directive and relatively spectrally inefficient transmission regimes, while still offering enormous capacity gains over current sub-6GHz alternatives. Building on this assumption, and supported by extensive indoor directional channel measurements at 160 GHz, this study advocates the use of very simple modulation and equalization techniques for sub-THz mobile access. Specifically, we demonstrate that, under the aforementioned transmission regimes, little or no equalization is needed for scoring significant capacity gain targets. In particular, we show that single-carrier or low-number-of-subcarriers modulations are very attractive competitors to the dramatically more complex and energy inefficient traditional multi-carrier designs.

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