Maximum Ergodic Capacity of Intelligent Reflecting Surface Assisted MIMO Wireless Communication System

Guo, Chang; Lu, Zhufei; Guo, Zhe; Yang, Feng; Ding, Lianghui

doi:10.1007/978-3-030-41114-5_25

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…, where |˜ n | 2 = 1 represents the unit modulus hardware constraint of the nth RIS element [4], ∀n = 1, 2, . .…”

Section: System Model: As Illustrated Inmentioning

confidence: 99%

“…To achieve the aforementioned benefit of RIS, the phase shifts of RIS elements should be properly optimized to realize passive beamforming at the RIS along with active beamforming at the BS. Some recent works on the sum-rate maximization problem are available by jointly optimizing the active and passive beamforming [4][5][6]. For instance, the authors in [4] presented a semidefinite relaxation (SDR) method to maximize the sum-rate of the entire system under the consideration of hardware constraints.…”

mentioning

confidence: 99%

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Low‐complexity joint active and passive beamforming for RIS‐aided MIMO systems

Siddiqi

Mir

Hao

et al. 2021

Electronics Letters

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In this paper, we consider the reconfigurable intelligent surface (RIS)aided MIMO systems to realize a high quality link between the base station (BS) and the users via a RIS. To achieve this goal, the active beamforming at the BS and the passive beamforming at the RIS should be jointly optimized. However, most of the existing joint optimization schemes that maximize the sum-rate have high computational complexity due to the complex derivative calculations and matrix inversion. To this end, a biologically inspired particle swarm optimization (PSO) algorithm is exploited to solve the non-convex sum-rate optimization problem with low-complexity. Simulation results show that the proposed scheme provides a better trade-off between performance and complexity than existing solutions for RIS-aided MIMO systems.

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“…, where |˜ n | 2 = 1 represents the unit modulus hardware constraint of the nth RIS element [4], ∀n = 1, 2, . .…”

Section: System Model: As Illustrated Inmentioning

confidence: 99%

mentioning

confidence: 99%

Low‐complexity joint active and passive beamforming for RIS‐aided MIMO systems

Siddiqi

Mir

Hao

et al. 2021

Electronics Letters

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“…Nevertheless, the integration of IRS into existing wireless networks faces new challenges, one of which is the optimization of the IRS reflection coefficients for facilitating the coherent superposition of dispersed intended signals and/or interference cancellation. Specifically, the ★ [18]-2020 [19]-2020 [20]-2020 [21]-2020 [22]-2019 [23]-2020 [24]-2019 [25]-2020 optimization of the IRS reflection coefficients is generally coupled with the transmit beamforming design, which is different from the conventional systems without IRS [26]. In [10], Wu et al considered the IRS-assisted multi-input single-output (MISO) and multi-user MISO systems by formulating the transmit power minimizing problem, subject to the individual signal-tointerference-plus-noise-ratio (SINR) requirements at each user.…”

Section: Introductionmentioning

confidence: 99%

The Optimal Pilot Power Allocation Strategy for Multi-IRS Assisted Communication Systems

An¹,

Xu²,

Gan³

et al. 2021

Preprint

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Intelligent reflecting surface (IRS) is a promising technology that enables the precise control of the electromagnetic environment in future wireless communication networks. To leverage the IRS effectively, the acquisition of channel state information (CSI) is crucial in IRS-assisted communication systems, which, however, is challenging. In this paper, we propose the optimal pilot power allocation strategy for the channel estimation of IRS-assisted communication systems, which is capable of further improving the achievable rate performance with imperfect CSI. More specifically, first of all, we introduce a multi-IRS assisted communication system in the face of practical channel estimation errors. Furthermore, the ergodic capacity with imperfect CSI is derived in an explicit closed-form expression under the singleinput single-output (SISO) consideration, which is dependent on the specific pilot power for estimating different reflection channels. Secondly, we formulate the optimization problem of maximizing the ergodic capacity with imperfect CSI, subject to the constraint of the average uplink pilot power. Thirdly, the method of Lagrange multipliers is invoked to solve the ergodic rate maximizing problem and thus to obtain the optimal pilot power allocation strategy. The resultant pilot power allocation solution suggests allocating more amount of power to the pilots for estimating the weak reflection channels, which is L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP

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“…In [11], the ergodic capacity (EC) was studied for SISO channels. Moreover, in [12], the EC for millimeter wave (mmWave) MIMO systems was investigated but not obtained in closed-form. Similarly, in [13], [14], only upper bounds for the EC were derived.…”

Section: Introductionmentioning

confidence: 99%

“…Also, [11] considered only SISO channels, while we focus on MIMO channels. Furthermore, [12] focused on mmWave systems and [13], [14] derived just upper bounds, while these works did not focus on the exact analysis of the EC. To this end, we have achieved to obtain the probability density function (PDF) of an IRS-assisted MIMO channel with correlation in a simple closedform expression, and we have optimized the EC by considering a practical phase-shift model.…”

Section: Introductionmentioning

confidence: 99%

Ergodic Capacity of IRS-Assisted MIMO Systems with Correlation and Practical Phase-Shift Modeling

Papazafeiropoulos¹

2021

Preprint

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We focus on the maximization of the exact ergodic capacity (EC) of a point-to-point multiple-input multiple-output (MIMO) system assisted by an intelligent reflecting surface (IRS). In addition, we account for the effects of correlated Rayleigh fading and the intertwinement between the amplitude and the phase shift of the reflecting coefficient of each IRS element, which are usually both neglected despite their presence in practice. Random matrix theory tools allow to derive the probability density function (PDF) of the cascaded channel in closed form, and subsequently, the EC, which depend only on the largescale statistics and the phase shifts. Notably, we optimize the EC with respect to the phase shifts with low overhead, i.e., once per several coherence intervals instead of the burden of frequent necessary optimization required by expressions being dependent on instantaneous channel information. Monte-Carlo (MC) simulations verify the analytical results and demonstrate the insightful interplay among the key parameters and their impact on the EC.

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Maximum Ergodic Capacity of Intelligent Reflecting Surface Assisted MIMO Wireless Communication System

Cited by 7 publications

References 16 publications

Low‐complexity joint active and passive beamforming for RIS‐aided MIMO systems

Low‐complexity joint active and passive beamforming for RIS‐aided MIMO systems

The Optimal Pilot Power Allocation Strategy for Multi-IRS Assisted Communication Systems

Ergodic Capacity of IRS-Assisted MIMO Systems with Correlation and Practical Phase-Shift Modeling

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