MISO Wireless Communication Systems via Intelligent Reflecting Surfaces (1904.12199v1)

Abstract: Intelligent reflecting surfaces (IRSs) have received considerable attention from the wireless communications research community recently. In particular, as low-cost passive devices, IRSs enable the control of the wireless propagation environment, which is not possible in conventional wireless networks. To take full advantage of such IRS-assisted communication systems, both the beamformer at the access point (AP) and the phase shifts at the IRS need to be optimally designed. However, thus far, the optimal design is not well understood. In this paper, a point-to-point IRS-assisted multiple-input single-output (MISO) communication system is investigated. The beamformer at the AP and the IRS phase shifts are jointly optimized to maximize the spectral efficiency. Two efficient algorithms exploiting fixed point iteration and manifold optimization techniques, respectively, are developed for solving the resulting non-convex optimization problem. The proposed algorithms not only achieve a higher spectral efficiency but also lead to a lower computational complexity than the state-of-the-art approach. Simulation results reveal that deploying large-scale IRSs in wireless systems is more efficient than increasing the antenna array size at the AP for enhancing both the spectral and the energy efficiency.

• The paper proposes a joint optimization of AP beamforming and IRS phase shifts using fixed point iteration and manifold optimization techniques.
• The study demonstrates significant gains in spectral efficiency and reduced computational complexity compared to SDR methods.
• This work highlights IRS technology's potential for energy-efficient, scalable, and cost-effective enhancements in future MISO wireless networks.

Intelligent Reflecting Surfaces in MISO Wireless Communication Systems

The paper presented in this paper explores an innovative approach for enhancing the performance of multiple-input single-output (MISO) wireless communication systems using intelligent reflecting surfaces (IRSs). IRSs represent a transformative technology in wireless networks that can control the wireless propagation environment and thus increase the spectral efficiency without heavily relying on additional energy-intensive infrastructure.

Key Contributions and Findings

This paper addresses a significant gap in the design and optimization of IRS-assisted MISO communication systems. The main contribution is the joint optimization of the beamformer at the access point (AP) and the phase shifts at the IRS, specifically aiming to maximize spectral efficiency. Two algorithms are proposed for this purpose:

1. Fixed Point Iteration Method: This approach iteratively optimizes the phase shifts to reach locally optimal solutions while ensuring reduced computational complexity compared to contemporary methods.
2. Manifold Optimization Technique: By recognizing the problem's geometric structure, namely the Riemannian manifold defined by the phase shifts, this approach ensures efficient navigation towards locally optimal solutions using state-of-the-art manifold optimization techniques.

Both algorithms outperform the conventional semidefinite relaxation (SDR) method in terms of spectral efficiency and computational efficiency, especially for large-scale IRS deployments. Simulation results demonstrate the superior performance of the proposed algorithms, illustrating a notable enhancement in spectral efficiency under varying user locations. Specifically, when the user is situated at a greater distance from both the AP and the IRS, the joint optimization becomes crucial, and the proposed solutions provide a marked advantage over existing methods.

Implications for Future Wireless Networks

The findings have profound implications for the design of future wireless networks. By leveraging IRS technology, network designers can achieve higher spectral and energy efficiency without the need for deploying additional antennas or RF chains, translating to cost-effective and greener network solutions. Furthermore, the potential to deploy IRSs on building facades highlights the scalability and ease of integration within urban landscapes.

Theoretical Contributions and Practical Considerations

Theoretically, this research advances the understanding of non-convex optimization in IRS-assisted systems by providing solutions that directly address the unit modulus constraints—an intrinsic challenge in optimizing IRS phase shifts. Practically, the reduced computational load of the proposed algorithms makes them suitable for real-time applications, thereby enhancing the feasibility of IRS deployment in dynamic network environments.

Future Directions

Looking ahead, further research could explore the integration of machine learning techniques to predict optimal IRS configurations dynamically, potentially leading to adaptive systems capable of real-time reconfiguration. Moreover, extending the current framework to multi-user and wideband systems could offer additional insights into IRS capabilities in more complex scenarios. As IRS technology matures, collaboration with hardware designers will be crucial to realize the theoretical gains posited in this paper through efficient, real-world implementations.

In summary, this paper lays a robust foundation for the integration of IRSs in future wireless communication systems, positing them as a viable solution to meet the growing demands for high data rate and energy-efficient networks.