Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization (1907.06002v4)

Abstract: Intelligent reflecting surface (IRS) that enables the control of the wireless propagation environment has been looked upon as a promising technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Applying this new model to an IRS-aided wireless system, we formulate a problem to maximize its achievable rate by jointly optimizing the transmit beamforming and the IRS reflect beamforming. The formulated problem is non-convex and difficult to be optimally solved in general, for which we propose a low-complexity suboptimal solution based on the alternating optimization (AO) technique. Simulation results unveil a substantial performance gain achieved by the joint beamforming optimization based on the proposed phase shift model as compared to the conventional ideal model.

• The paper introduces a novel phase shift model that accounts for amplitude variations alongside phase adjustments in IRS implementations.
• It employs an alternating optimization method to jointly optimize transmit and reflect beamforming in IRS-aided MISO systems.
• Simulation results reveal significant performance gains over ideal models, highlighting the need for hardware-aware IRS design.

Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization

The paper "Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization" addresses the challenges and potentials of utilizing Intelligent Reflecting Surfaces (IRS) in wireless communication systems. By presenting a novel phase shift model along with a beamforming optimization strategy, the authors provide insights into improving spectrum and energy efficiency, which are critical factors in future wireless communications.

Summary and Contributions

The paper departs from the traditional assumption of ideal phase shift models—where the amplitude remains constant regardless of phase shift—by proposing a practical model that accounts for amplitude variations dependent on phase shifts. This approach better reflects hardware realities and limitations, such as those posed by semiconductor devices, metallic, and dielectric substrates inherent in practical IRS implementations.

The authors address the IRS-aided MISO (Multiple-Input Single-Output) system to demonstrate their model's impact. Their objective is to maximize the achievable rate by jointly optimizing transmit and reflect beamforming. The non-convex nature of the problem is tackled using a suboptimal solution based on an alternating optimization (AO) technique. The behavior of their phase shift model is corroborated with experimental data, validating its applicability and accuracy.

Key Results

The simulation results demonstrate substantial performance gains using the proposed phase shift model over traditional ideal models, particularly as the user moves closer to the IRS, increasing the channel gains via IRS reflection. When varying the number of IRS reflecting elements, the model consistently shows enhanced performance. These simulations underscore the necessity of incorporating practical considerations in IRS designs, as they impact real-world efficacy significantly.

Implications and Future Developments

The findings suggest that IRS configurations in future wireless systems must consider hardware constraints to optimize performance effectively. Practically, this requires a shift in how IRS phase shifts are employed and analyzed.

Moreover, from a theoretical perspective, the paper opens pathways for more refined IRS models and algorithm designs that balance amplitude and phase adjustments in beamforming. Potential extensions of this research could delve into more complex scenarios, such as multi-user setups, OFDM systems, and IRS-assisted networks with additional constraints like security or simultaneous wireless information and power transfer (SWIPT).

Conclusion

By shifting the perspective from idealized models to practical implementations, this paper contributes to a more grounded understanding of IRS technology's potential and challenges. Future research building on these insights could significantly influence the development and deployment of IRS-based systems in next-generation wireless communication networks.