Performance Analysis of Joint Radar and Communication using OFDM and OTFS

Gaudio, Lorenzo; Kobayashi, Mari; Bissinger, Björn; Caire, Giuseppe

doi:10.1109/iccw.2019.8757044

Cited by 86 publications

(50 citation statements)

References 11 publications

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“…For the LoS channel in Eq. (4), the path-loss follows the model in [9, (5) we use again the model in [8] and the LoS probability is evaluated following [10]. The quantity α T in Eq.…”

Section: Numerical Resultsmentioning

confidence: 99%

Using Massive MIMO Arrays for Joint Communication and Sensing

Buzzi

D’Andrea

Lops

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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One of the trends that is gaining more and more importance in the field of beyond-5G and 6G wireless communication systems is the investigation on systems that jointly perform communication and sensing of the environment. This paper proposes to use a base station (BS), that we call radar-BS, equipped with a large-scale antenna array to execute, using the same frequency range, communication with mobile users and sensing/surveillance of the surrounding environment through radar scanning. The massive antenna array can indeed both operate as a MIMO radar with co-located antennastransmitting radar signals pointing at positive elevation angles -and perform signal-space beamforming to communicate with users mainly based on the ground. Our results show that using a massive MIMO radar-BS the communication and the radar system can coexist with little mutual interference.Index Terms-massive MIMO, radar, joint communications and sensing.

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“…For the LoS channel in Eq. (4), the path-loss follows the model in [9, (5) we use again the model in [8] and the LoS probability is evaluated following [10]. The quantity α T in Eq.…”

Section: Numerical Resultsmentioning

confidence: 99%

Using Massive MIMO Arrays for Joint Communication and Sensing

Buzzi

D’Andrea

Lops

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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show abstract

“…Another research direction is to thoroughly quantify the impact of the parameters of a given statistical channel model on the mutual information obtained by Theorems 1 and 2. In this respect, an attention can be devoted to the Doppler shift as in the recent paper [8] and in the references therein. Finally, transmission schemes with a partial channel knowledge at the receiver, or scenarios with different delay constraints deserve a particular attention.…”

Section: Resultsmentioning

confidence: 99%

Mutual Information of Wireless Channels and Block-Jacobi Ergodic Operators

Hachem

Hardy

Shamai

2019

IEEE Trans. Inform. Theory

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Shannon's mutual information of a random multiple antenna and multipath time varying channel is studied in the general case where the process constructed from the channel coefficients is an ergodic and stationary process which is assumed to be available at the receiver. From this viewpoint, the channel can also be represented by an ergodic self-adjoint block-Jacobi operator, which is close in many aspects to a block version of a random Schrödinger operator. The mutual information is then related to the so-called density of states of this operator. In this paper, it is shown that under the weakest assumptions on the channel, the mutual information can be expressed in terms of a matrix-valued stochastic process coupled with the channel process. This allows numerical approximations of the mutual information in this general setting. Moreover, assuming further that the channel coefficient process is a Markov process, a representation for the mutual information offset in the large Signal to Noise Ratio regime is obtained in terms of another related Markov process. This generalizes previous results from Levy et.al. [18,19]. It is also illustrated how the mutual information expressions that are closely related to those predicted by the random matrix theory can be recovered in the large dimensional regime.

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“…where r = x − y , G is the transmit antenna gain, h 0 (τ ) denotes the channel power gain of the link between the BS and the object during time slot τ , A is the radar cross-section area of the object, A e is the effective area i.e., A e = G c 2 4πf 2 , where c is the speed of light and f is the carrier frequency, and = (c/4πf ) 2 . Then, by focusing on a particular time slot, the signal-to-interference-plus-noise ratio (SINR) of the reflected pilot sequence, is given by…”

Section: Detection Modelmentioning

confidence: 99%

“…E MERGING applications such as smart cars, unmanned aerial vehicles (UAV) and enhanced localization, lead to an ever-increasing demand for systems with both communication and radar sensing capabilities [1], [2]. In order to address this demand, joint communication and radar sensing (JCAS) techniques have been developed, integrating the two operations of communication and sensing over a shared spectrum.…”

Section: Introductionmentioning

confidence: 99%

FD-JCAS Techniques for mmWave HetNets: Ginibre Point Process Modeling and Analysis

Skouroumounis

Psomas

Krikidis

2022

IEEE Trans. on Mobile Comput.

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In this paper, we study the co-design of full-duplex (FD) radio with joint communication and radar sensing (JCAS) techniques in millimeter-wave (mmWave) heterogeneous networks (HetNets). Spectral co-existence of radar and communication systems causes mutual interference between the two systems, compromising both the data exchange and sensing capabilities. Focusing on the detection performance, we propose a cooperative detection technique, which exploits the sensing information from multiple base stations (BSs), aiming at enhancing the probability of successfully detecting an object. Three combining rules are considered, namely the OR, the Majority and the AND rule. In real-world network scenarios, the locations of the BSs are spatially correlated, exhibiting a repulsive behavior. Therefore, we model the spatial distribution of the BSs as a β-Ginibre point process (β-GPP), which can characterize the repulsion among the BSs. By using stochastic geometry tools, analytical expressions for the detection performance of β-GPP-based FD-JCAS systems are expressed for each of the considered combining rule. Furthermore, by considering temporal interference correlation, we evaluate the probability of successfully detecting an object over two different time slots. Our results demonstrate that our proposed technique can significantly improve the detection performance when compared to the conventional non-cooperative technique.

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Performance Analysis of Joint Radar and Communication using OFDM and OTFS

Cited by 86 publications

References 11 publications

Using Massive MIMO Arrays for Joint Communication and Sensing

Using Massive MIMO Arrays for Joint Communication and Sensing

Mutual Information of Wireless Channels and Block-Jacobi Ergodic Operators

FD-JCAS Techniques for mmWave HetNets: Ginibre Point Process Modeling and Analysis

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