On directional modulation: An analysis of transmission scheme with multiple directions

Hafez, Mohammed; Arslan, Hüseyin

doi:10.1109/iccw.2015.7247222

Cited by 29 publications

(29 citation statements)

References 19 publications

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“…In [88], the singular value decomposition (SVD) is used to directionally modulate symbols in a two user system. The authors of [90] derive the array weights to create two orthogonal far field patterns to directionally modulate two symbols to two different locations and [92] uses least-norm to derive the array weights and directionally modulate symbols towards multiple destinations in a multi-user multi-input multi-output (MIMO) system. Later, [93] considers using ZF precoder to directionally modulate symbols and provide security for multiple single-antenna legitimate receivers in the presence of multiple single-antenna eavesdroppers.…”

Section: A Symbol-level Precoding For Directional Modulationmentioning

confidence: 99%

Symbol-Level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-Art, Classification, and Challenges

Alodeh

Spano

Kalantari

et al. 2018

IEEE Commun. Surv. Tutorials

138

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Precoding has been conventionally considered as an effective means of mitigating the interference and efficiently exploiting the available in the multiantenna downlink channel, where multiple users are simultaneously served with independent information over the same channel resources. The early works in this area were focused on transmitting an individual information stream to each user by constructing weighted linear combinations of symbol blocks (codewords). However, more recent works have moved beyond this traditional view by: i) transmitting distinct data streams to groups of users and ii) applying precoding on a symbol-per-symbol basis. In this context, the current survey presents a unified view and classification of precoding techniques with respect to two main axes: i) the switching rate of the precoding weights, leading to the classes of block-and symbol-level precoding, ii) the number of users that each stream is addressed to, hence unicast-/multicast-/broadcast-precoding. Furthermore, the classified techniques are compared through representative numerical results to demonstrate their relative performance and uncover fundamental insights. Finally, a list of open theoretical problems and practical challenges are presented to inspire further research in this area. 1

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Section: A Symbol-level Precoding For Directional Modulationmentioning

confidence: 99%

Symbol-Level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-Art, Classification, and Challenges

Alodeh

Spano

Kalantari

et al. 2018

IEEE Commun. Surv. Tutorials

138

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“…The guard units in time or frequency domains and other extra overheads decrease the spectral efficiency, which is critical especially for the eMBB type of communications. Furthermore, the multi-antenna techniques [11] such as beamforming and massive MIMO is another crucial aspect to utilize the lattice more efficiently. However, the self-ISI and self-ICI of a waveform prevents to apply MIMO techniques in a straight-forward way and increases complexity significantly.…”

Section: B 5g Use Cases and Waveform Design Requirementsmentioning

confidence: 99%

Waveform Design for 5G and Beyond

Demir

Elkourdi

Ibrahim

et al. 2018

5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management

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The standardization activities of wireless mobile telecommunications have begun with analog standards that were introduced in the 1980s, and a new generation develops almost every 10 years to meet the exponentially growing market demand. The leap from analog to digital started in second-generation (2G) systems, along with the use of mobile data services. The 3G digital evolution enabled video calls and global positioning system (GPS) services on mobile devices. The 4G systems pushed the limits of data services further by better exploiting the time-frequency resources using orthogonal frequency-division multiple access (OFDMA) as an air interface [1]. Recently, the International Telecommunications Union (ITU) has defined the expectations for 5G [2], and the study of the nextgeneration wireless system is in progress with a harmony between academia, industry, and standardization entities to accomplish its first deployment in 2020.5G is envisioned to improve major key performance indicators (KPIs), such as peak data rate, spectral efficiency, power consumption, complexity, connection density, latency, and mobility, significantly. Furthermore, the new standard should support a diverse range of services all under the same network [3]. The IMT-2020 vision defines the use cases into three main categories as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC) featuring 20 Gb/s peak data rate, 10 6 /km 2 device density, and less than 1 ms latency, respectively [4]. A flexible air interface is required to meet these different requirements. As a result, the waveform, which is the main component of any air interface, has to be designed precisely to facilitate such flexibility [5].This chapter aims to provide a complete picture of the ongoing 5G waveform discussions and overviews the major candidates. The chapter is organized as follows: Section II provides a brief description of the waveform and reveals the 5G use cases and waveform design requirements. Also, this section presents the main features of CP-OFDM that is currently deployed in 4G LTE systems. CP-OFDM is the baseline of the 5G waveform discussions since the performance of a new waveform is usually compared with it. Section III examines the essential characteristics of the major waveform candidates along with the related advantages and disadvantages. Section IV summarizes and compares the key features of the waveforms. Finally, Section V concludes the chapter. This is the pre-peer reviewed version of the article which has been published in final form at [https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119333142.ch2]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

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“…An orthogonal projection (OP) method in [10] was designed for the multi-beam DM systems. And in [14], the authors proposed a multiple-direction DM transmission scheme which employed the space domain to transmit the multiple data streams independently and increased the capacity of the system.…”

Section: Introductionmentioning

confidence: 99%

Robust Directional Modulation Design for Secrecy Rate Maximization in Multiuser Networks

Gui

Zhou

Shu

et al. 2020

IEEE Systems Journal

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In this paper, based on directional modulation (DM), robust beamforming matrix design for sum secrecy rate maximization is investigated in multi-user systems. The base station (BS) is assumed to have the imperfect knowledge of the direction angle toward each eavesdropper, with the estimation error following the Von Mises distribution. To this end, a Von Mises distribution-Sum Secrecy Rate Maximization (VMD-SSRM) method is proposed to maximize the sum secrecy rate by employing semi-definite relaxation and first-order approximation based on Taylor expansion to solve the optimization problem. Then in order to optimize the sum secrecy rate in the case of the worst estimation error of direction angle toward each eavesdropper, we propose a maximum angle estimation error-SSRM (MAEE-SSRM) method. The optimization problem is constructed based on the upper and lower bounds of the estimated eavesdropping channel related coefficient and then solved by the change of the variable method. Simulation results show that our two proposed methods have better sum secrecy rate than zero-forcing (ZF) method and signal-to-leakage-andnoise ratio (SLNR) method. Furthermore, the sum secrecy rate performance of our VMD-SSRM method is better than that of our MAEE-SSRM method.

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On directional modulation: An analysis of transmission scheme with multiple directions

Cited by 29 publications

References 19 publications

Symbol-Level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-Art, Classification, and Challenges

Symbol-Level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-Art, Classification, and Challenges

Waveform Design for 5G and Beyond

Robust Directional Modulation Design for Secrecy Rate Maximization in Multiuser Networks

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