Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization

Mu, Xidong; Liu, Yuanwei; Guo, Li; Lin, Jiaru; Al-Dhahir, Naofal

doi:10.1109/twc.2020.3006915

Cited by 325 publications

(191 citation statements)

References 46 publications

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“…Given the sparse formulation in (20), several compressive sensing reconstruction algorithms, such as orthogonal matching pursuit (OMP) [35], [36], can be employed to find an approximate solution for x β,k . With this solution for x β,k , the full channel vector h T,k can be constructed according to (16). Finally, the constructed full channel vector can be used to find the best LIS reflection beamforming vector, ψ n CS ∈ P, out of the codebook P, via an offline search using (8).…”

Section: A Recovering Full Channels From Sampled Channelsmentioning

confidence: 99%

“…In terms of the overall system performance, an uplink multiuser scenario was considered in [11] and the data rates were formulated for the case where channel estimation errors exist in the available channel knowledge. A downlink LIS-assisted multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) framework is proposed in [16] for achieving higher system spectrum efficiency gains. The LIS can be leveraged for wireless localization purposes as well; in [17], an LIS-assisted downlink millimeter wave (mmWave) positioning problem was analyzed from the Fisher Information perspective.…”

Section: Introductionmentioning

confidence: 99%

“…The Critical Challenge: All the prior work in [5], [6], [9]- [11], [16] assumed that the knowledge about the channels between the LIS and the transmitters/receivers is available at the base station, either perfectly or with some error. Obtaining this channel knowledge, however, is one of the most crucial challenges for LIS systems because of the massive number of antennas (LIS elements) and the hardware constraints on these elements.…”

Section: Introductionmentioning

confidence: 99%

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Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

2021

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Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full channel knowledge. Estimating these channels at the LIS, however, is a key challenging problem. With the massive number of LIS elements, channel estimation or reflection beam training will be associated with (i) huge training overhead if all the LIS elements are passive (not connected to a baseband) or with (ii) prohibitive hardware complexity and power consumption if all the elements are connected to the baseband through a fully-digital or hybrid analog/digital architecture. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. In the second approach, we develop a deep-learning based solution where the LIS learns how to interact with the incident signal given the channels at the active elements, which represent the state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems. INDEX TERMS large intelligent surface, intelligent reflecting surfaces, reconfigurable intelligent surface, smart reflect-array, beamforming, millimeter wave, compressive sensing, deep learning.

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Section: A Recovering Full Channels From Sampled Channelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

2021

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“…e detail is similar to [26], which is omitted for conciseness. In fact, simulations show that the optimal matrices V 1 and V 2 are always Rank-1 only with the SDR method.…”

Section: (18)mentioning

confidence: 99%

Enhanced Secure SWIPT in Heterogeneous Network via Intelligent Reflecting Surface

Yang

Huang

et al. 2021

Security and Communication Networks

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In this paper, secure transmission in a simultaneous wireless information and power transfer technology-enabled heterogeneous network with the aid of multiple IRSs is investigated. As a potential technology for 6G, intelligent reflecting surface (IRS) brings more spatial degrees of freedom to enhance physical layer security. Our goal is to maximize the secrecy rate by carefully designing the transmit beamforming vector, artificial noise vector, and reflecting coefficients under the constraint of quality-of-service. The formulated problem is hard to solve due to the nonconcave objective function as well as the coupling variables and unit-modulus constraints. Fortunately, by using alternating optimization, successive convex approximation, and sequential Rank-1 constraint relaxation approach, the original problem is transformed into convex form and a suboptimal solution is achieved. Numerical results show that the proposed scheme outperforms other existing benchmark schemes without IRS and can maintain promising security performance as the number of terminals increases with lower energy consumption.

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“…For the IRS-aided NOMA systems, [35][36][37][38][39][40] investigate the IRS-aided downlink NOMA communication. In [35], a simple design of IRS-NOMA transmission is proposed.…”

mentioning

confidence: 99%

Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array

Liu

Cheng

et al. 2021

IEEE Trans. Veh. Technol.

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In this paper, we propose a downlink intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) for millimeter-wave (mmWave) massive MIMO with lens antenna array, i.e., IRS-aided mmWave beamspace NOMA, where the single-antenna users without direct-link but connected to the base station (BS) with the aid of the IRS are grouped as one NOMA group. Considering the power leakage problem in beamspace channel and the per-antenna power constraint, we propose two multi-beam selection strategies for the BS-IRS link under two channel models, i.e., 2-dimension (2D) channel model and 3-dimension (3D) channel model, respectively, where two corresponding RF chain configuration strategies are designed, respectively. Then, we formulate and solve the optimization problem for maximizing the weighted sum rate by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, where we propose the alternating optimization (AO) method to solve the above joint optimization problem. Especially, different from the stochastic method, based on the beam-splitting technique, we propose the method to initialize the feasible solution for the proposed AO method, where the transmit power minimization problem is formulated and solved. Through simulations, the weighted sum rate performance of the proposed IRS-aided mmWave beamspace NOMA is verified.

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Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization

Cited by 325 publications

References 46 publications

Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

Enhanced Secure SWIPT in Heterogeneous Network via Intelligent Reflecting Surface

Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array

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