Adaptive Beamforming With Resource Allocation for Distance-Aware Multi-User Indoor Terahertz Communications

Lin, Caijin; Li, Geoffrey Ye

doi:10.1109/tcomm.2015.2440356

Cited by 130 publications

(114 citation statements)

References 35 publications

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“…In this section, we present the numerical result to show the secrecy rate performance of the proposed . To construct the channels h ru , h re , H t , h du , and h de , based on the study,17 we consider a modified geometric channel model to capture the characteristics of the THz channel, which is comprised several paths by reflecting propagations and directly transmissions. Specifically, for the channel

boldH \in C^{N_{t} \times N_{r}}

, the channel response can be written as

boldH = \sqrt{\frac{N_{t} N_{r}}{L}} \sum_{l = 1}^{L} α_{l} a_{t} ((), φ_{l}^{t}) a_{r}^{H} ((), φ_{l}^{r}),

where L is the number of paths for channel H ,

φ_{l}^{t}

(respectively,

φ_{l}^{r}

) is the azimuth angle of departure (respectively, arrival) for path l , and

a_{t} ((), φ_{l}^{t})

(respectively,

a_{r} ((), φ_{l}^{r})

) is the array steering vector at transmitter (respectively, receiver), and α l is the complex gain for path l , which is mainly contributed by transmission losses and molecular absorption losses in the THz communication.…”

Section: Numerical Resultsmentioning

confidence: 99%

Secrecy rate optimization for intelligent reflecting surface aided multi‐input‐single‐output terahertz communication

Chen

et al. 2020

Micro & Optical Tech Letters

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Intelligent reflecting surface (IRS) is an emerging paradigm to enhance terahertz (THz) communications with low hardware costs and reduced power consumptions. In this work, we study an IRS‐aided THz multi‐input‐single‐output secure communication system, where a base station (BS) transmits confidential signals to a single‐antenna user with the presence of a single‐antenna eavesdropper. We seek to maximize the secrecy rate by designing the discrete phase‐shifts at the IRS and the precoder at the BS, which is an intractable nonconvex optimization problem. To solve it, we propose to iteratively design the above two matrix variables. Specifically, with a given phase‐shift matrix, we can obtain the optimal precoder by the Rayleigh‐Ritz theorem. And with a given precoder matrix, we propose a cross‐entropy‐based algorithm to obtain the optimal phase‐shift matrix. Numerical results demonstrate that our proposed solution is able to improve the secrecy rate than that of the case without IRS.

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boldH \in C^{N_{t} \times N_{r}}

, the channel response can be written as

boldH = \sqrt{\frac{N_{t} N_{r}}{L}} \sum_{l = 1}^{L} α_{l} a_{t} ((), φ_{l}^{t}) a_{r}^{H} ((), φ_{l}^{r}),

where L is the number of paths for channel H ,

φ_{l}^{t}

(respectively,

φ_{l}^{r}

) is the azimuth angle of departure (respectively, arrival) for path l , and

a_{t} ((), φ_{l}^{t})

(respectively,

a_{r} ((), φ_{l}^{r})

Section: Numerical Resultsmentioning

confidence: 99%

Secrecy rate optimization for intelligent reflecting surface aided multi‐input‐single‐output terahertz communication

Chen

et al. 2020

Micro & Optical Tech Letters

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“…In this section, numerical results are presented to demonstrate the performance of . For the channel H t , H r and H d , we assume L = 3, f = 220GHz, φ t and φ r follow the second order Gaussian mixture models in range [0, 2 π ] , and the antenna gain for the BS and receiver are all 30dBi. Besides, we assume the BS, IRS, and the receiver are located at an equilateral triangle whose side length is 50 m, and we consider both the BS and user are equipped with a uniform linear array.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…To construct the channels H r , H t , and H d , based on Reference , we imply a geometrical channel model to capture the peculiarities of the THz channel, where the channel is comprised of several paths. Specifically, for the channel

boldH \in C^{N_{t} \times N_{r}}

, the channel response can be written as

boldH = \sqrt{\frac{N_{t} N_{r}}{L}} \sum_{l = 1}^{L} α_{l} G_{t} G_{r} a_{t} ((), φ_{l}^{t}) a_{r}^{H} ((), φ_{l}^{r}),

where L is the number of paths, G t (resp.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Spectral efficiency optimization for intelligent reflecting surface aided multi‐input multi‐output terahertz system

Chen

et al. 2020

Micro & Optical Tech Letters

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Intelligent reflecting surface (IRS) is considered as a promising application in terahertz (THz) communications since it is able to enhance the THz communication with no additional power consumptions. In this letter, we consider a downlink multi‐input multi‐output system, where a base station (BS) communicates to a user with the aid of the IRS. Our goal is to optimize the spectral efficiency by designing discrete phase‐shifts at the IRS and the precoder at the BS, which is an intractable nonconvex optimization problem. To solve it, we approximate the problem into another form where we maximize the sum of path gains by designing the phase‐shifts of the IRS, and we propose a cross‐entropy based algorithm to search the optimal IRS phase‐shifts matrix. After that, we use the classical singular value decomposition and water‐filling solution to design the optimal precoder.

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“…where Hybrid beamforming often entails user grouping and SA selection. In [112], a THz multi-carrier distance-dependent hybrid beamforming scheme is proposed, in which user grouping is achieved through analog beamforming, alongside digital beamforming, power allocation, and SA selection mechanisms. The proposed solution allows users of different user groups to share frequencies while avoiding interference in the analog domain.…”

Section: A Thz Hybrid Beamformingmentioning

confidence: 99%

An Overview of Signal Processing Techniques for Terahertz Communications

Sarieddeen¹,

Alouini²,

Al-Naffouri³

2020

Preprint

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Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

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Adaptive Beamforming With Resource Allocation for Distance-Aware Multi-User Indoor Terahertz Communications

Cited by 130 publications

References 35 publications

Secrecy rate optimization for intelligent reflecting surface aided multi‐input‐single‐output terahertz communication

Secrecy rate optimization for intelligent reflecting surface aided multi‐input‐single‐output terahertz communication

Spectral efficiency optimization for intelligent reflecting surface aided multi‐input multi‐output terahertz system

An Overview of Signal Processing Techniques for Terahertz Communications

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