[Paper Review] Intelligent Reflecting Surface Enhanced Wireless Network: Two-timescale Beamforming Optimization

Minjian Zhao, Qingqing Wu+2 more|arXiv (Cornell University)|Dec 4, 2019

Advanced Wireless Communication Technologies47 references70 citations

TL;DR

Proposes a two-timescale (TTS) beamforming framework for IRS-aided multiuser MISO systems, optimizing long-term discrete IRS phase shifts with S-CSI and short-term active precoding with I-CSI to maximize average sum-rate.

ABSTRACT

Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase-shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI, while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users' effective channels with the optimized IRS phase-shifts, thus significantly reducing the channel training overhead and passive beamforming complexity over the existing schemes based on the I-CSI of all channels. For the single-user case, a novel penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase-shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS optimization algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.

Motivation & Objective

• Motivate and address the challenge of IRS channel estimation and beamforming with large IRSs under practical discrete phase shifts.
• Develop a two-timescale transmission protocol that uses statistical CSI for IRS configuration and instantaneous CSI for AP precoding.
• Propose efficient algorithms for single-user (PDD-based) and multiuser (SSCA-based) scenarios to optimize the TTS problem.
• Analyze the impact of channel correlation and deterministic components on TTS performance.
• Demonstrate significant rate gains of IRS with S-CSI over systems without IRS under practical constraints.

Proposed method

• Model a multiuser MISO downlink with an IRS having N elements and an AP with M antennas.
• Adopt a correlated Rician fading channel model for AP-IRS, IRS-user, and AP-user links.
• Formulate a two-timescale optimization problem to maximize the average weighted sum-rate with long-term IRS phase shifts and short-term precoding.
• For the single-user case, derive an upper bound on the average rate and solve a deterministic non-convex problem via a parallelizable PDD-based algorithm.
• For the multiuser case, develop a TTS SSCA algorithm that builds a quadratic surrogate of the objective and solves the resulting problem with Lagrange dual methods, while using WMMSE for fixed IRS phases.
• Incorporate discrete phase shifts by restricting phase values to a finite set and discuss the impact of S-CSI versus I-CSI.

Experimental results

Research questions

• RQ1 How can IRS phase shifts be optimally designed using only statistical CSI to reduce training and computation?
• RQ2 How should transmit precoding at the AP be adapted in each time slot given instantaneous effective channels with fixed IRS phases?
• RQ3 What are the performance gains of the two-timescale scheme under discrete phase shifts and correlated Rician channels?
• RQ4 How do channel deterministic components and spatial correlation affect the TTS optimization performance?
• RQ5 Can parallelizable algorithms (PDD, SSCA) provide near-optimal solutions with lower complexity compared to full I-CSI-based designs?

Key findings

• The proposed TTS scheme reduces training overhead and passive beamforming complexity by separating long-term IRS design (S-CSI) from short-term AP precoding (I-CSI).
• A PDD-based algorithm enables parallel updates of IRS phase shifts in the single-user case, achieving near-optimal performance under discrete phase shifts.
• A general SSCA-based algorithm is developed for the multiuser case, using a quadratic surrogate and Lagrange dual method to update IRS phases and WMMSE for short-term precoding.
• Simulations show that IRS with S-CSI can significantly improve rate over systems without IRS, and larger deterministic components or higher correlation can lessen rate loss from using S-CSI instead of ideal I-CSI.
• The framework accommodates discrete phase shifts and demonstrates practical performance gains with reduced training and computational burden.