Intelligent Reflecting Surface Enhanced Wireless Network: Two-timescale Beamforming Optimization (1912.01818v2)

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.

• The paper introduces a two-timescale beamforming framework that uses long-term statistical CSI for IRS phase optimization and short-term instantaneous CSI for transmit precoding.
• It proposes efficient algorithms—a penalty dual decomposition method for single-user scenarios and a stochastic successive convex approximation for multiuser cases—to handle discrete phase shifts.
• Simulations validate significant rate improvements in correlated Rician fading environments, emphasizing IRS's potential to enhance next-generation wireless networks.

Intelligent Reflecting Surface Enhanced Wireless Network: Two-Timescale Beamforming Optimization

The paper presents a sophisticated exploration into the application of Intelligent Reflecting Surfaces (IRS) within wireless network environments, focusing on the development of a two-timescale (TTS) beamforming optimization strategy. The central theme revolves around the challenge of optimizing beamforming in an IRS-enhanced multiuser system, under the constraints of statistical channel state information (S-CSI), with discrete phase shift adjustments.

The researchers approach the IRS-aided wireless communication problem by acknowledging the practical difficulties of acquiring instantaneous channel state information (I-CSI) due to the passive nature and sheer volume of reflecting elements inherent to IRS. To address this, the authors propose a TTS optimization framework that cleverly extracts long-term IRS phase shifts from S-CSI while dynamically adjusting short-term transmit precoding vectors based on available I-CSI from effective fading channels.

A crucial contribution of this paper is the analysis of a multiuser MISO system within correlated Rician fading environments. The paper adopts a multifaceted problem structure where the IRS phase adjustments are stipulated based on long-term statistical CSI, and AP transmit beamforming is acutely fine-tuned to instantaneous CSI. The authors include practical considerations such as discrete phase shifts, adding another layer of realism to the paper.

In single-user scenarios, the authors introduce a penalty dual decomposition (PDD)-based algorithm. This algorithm capitalizes on parallel updates to expedite convergence and efficiency, sidestepping the computational intensive nature of other methods like semidefinite relaxation (SDR). The strong numerical results demonstrate that the PDD-based method achieves near-optimal performance, as evidenced by its performance approaching that of exhaustive search methods.

In the multiuser context, the paper expands on this strategy with a stochastic successive convex approximation (SSCA) algorithm. The SSCA iterates IRS phase evaluations using quadratic surrogate functions, constructed from strategically generated channel samples rather than exact channel laws, allowing the algorithm to evolve efficiently toward optimal beamforming configurations.

Simulation results provide insightful validation of the algorithms, indicating their effectiveness under varying conditions, particularly highlighting the role of channel correlations and Rician factors on the achievable rates. The analysis underscores significant rate improvements using IRS even when constrained to S-CSI and discrete shifts — showing that these systems can outperform traditional ones lacking IRS despite the inherent limitations.

The implications of this research are vast, notably in the reinforcement of IRS as a low-cost, energy-efficient augmentation to existing millimeter-wave and beyond-5G systems. The IRS's ability to shape wireless propagation environments suggests practical applications in enhancing cell-edge connectivity and supporting ultra-reliable communications within dense urban settings.

Future developments could see these algorithmic strategies expanded to accommodate even broader multi-user scenarios and novel forms of channel estimation errors. Moreover, the intersection of IRS technology with machine learning could potentially revolutionize predictive beamforming and further mitigate CSI acquisition challenges, opening new frontiers in adaptive and resilient wireless communication infrastructures.

In conclusion, applying IRS with TTS optimization based on S-CSI is demonstrated as not only feasible but also highly effective, setting a promising trajectory for IRS technology as a cornerstone in the future of wireless networks.