Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design (2003.00648v3)

Abstract: To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multi-user broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.

• The paper introduces two efficient channel estimation schemes, SiUCE and SeUCE, tailored for IRS-assisted multi-user OFDMA systems.
• It demonstrates that the SiUCE scheme minimizes training time with optimized pilot tone allocations, while the SeUCE scheme supports more users by leveraging a shared IRS-AP channel under LoS conditions.
• Numerical simulations validate significant SNR gains and improved channel estimation accuracy over traditional methods.

Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design

This paper presents two efficient channel estimation schemes tailored for an IRS-assisted multi-user OFDMA system. The critical challenge addressed is the need for accurate Channel State Information (CSI) to leverage the passive beamforming gains of IRS, which becomes increasingly complex with the number of IRS elements and users.

The paper introduces the Simultaneous-User Channel Estimation (SiUCE) and Sequential-User Channel Estimation (SeUCE) schemes. The SiUCE scheme processes the CSI of all users concurrently, suitable for scenarios with arbitrary frequency-selective fading channels, while SeUCE is proposed for scenarios where user-IRS channels are line-of-sight (LoS) dominant, exploiting the common IRS-AP channel across users to increase training efficiency.

Key Findings and Details

1. SiUCE Scheme:
   • This approach estimates all user channels concurrently at the access point, catering to arbitrary frequency-selective channels.
   • It demonstrates minimized training time and supports a maximum number of users given its optimized pilot tone allocations and IRS reflection pattern.
   • The SiUCE scheme can support up to $K_1 = \lfloor N / L \rfloor$ users, where $N$ is the number of sub-carriers and $L$ is the maximum delay spread.
2. SeUCE Scheme:
   • By exploiting the shared IRS-AP channel, this scheme first estimates the CSI of a reference user and then uses it to infer the CSI of other users.
   • The SeUCE scheme supports a greater number of users, $K_2 = \lfloor (M+1)(N-L)/(M+L) \rfloor + 1$, albeit with increased computational complexity compared to SiUCE.
   • The scheme is optimized further under the assumption of LoS dominant channels, reducing the necessary training overhead.
3. Training Designs:
   • An orthogonal IRS pattern employing a DFT-based approach leads to optimized training, which significantly outperforms traditional ON/OFF or random reflection patterns.
   • The paper provides numerical evidence indicating substantial SNR gains owing to the proposed pilot tone and training designs.
4. Comparative Performance:
   • Numerical simulations validate the superiority of both schemes over existing benchmark designs in terms of channel estimation accuracy.
   • The SiUCE offers simpler complexity but supports fewer users, while SeUCE is more complex but offers greater utility for environments favoring common IRS-AP channels.

Implications and Future Prospects

This work advances the utility of IRS in complex wireless communication environments, serving both theoretical exploration and practical system design. The implications reach beyond efficient utilization of frequencies and IRS configurations in OFDMA systems, charting pathways for more advanced predictive algorithms capable of exploiting IRS's inherent properties.

Moving forward, such schemes could be pivotal in further realizing IRS's role in next-gen networks like 6G, particularly in expanding coverage and connecting edge devices with precision and efficiency. Extensions could explore coherent designs that account for real-world imperfections, incorporating adaptive learning mechanisms to further enhance the robustness and adaptability of the channel estimation frameworks proposed.