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Remote Interference Mitigation through Null Precoding and Fractional Programming

Xuyang Sun, Hussein A. Ammar, Israfil Bahceci, Raviraj Adve, Gary Boudreau, Zehua Li

TL;DR

This work tackles remote interference from tropospheric ducting, which can disrupt uplink reception and channel estimation across distant cells. It develops an active RI mitigation framework combining RI-aware CE/UL processing with two precoding strategies: null precoding to suppress the LoS RI component and fractional-programming (FP) based precoding to balance victim UL performance with aggressor DL service, aided by root-MUSIC AoA estimation of RI. The results show substantial improvements in channel estimation NMSE (e.g., a reduction of about $5.23$ dB) and uplink spectral efficiency (up to about $5.8$ bit/s/Hz at low UL power), while preserving aggressor DL performance within acceptable limits. These findings demonstrate the feasibility of operating RI-affected networks at lower UL transmit powers and suggest FP as a robust method for RI suppression with manageable coordination requirements in large-scale deployments.

Abstract

With the rapid deployment of 5G systems, remote interference (RI) caused by atmospheric ducting has emerged as an occasional, but critical challenge. This phenomenon occurs when the downlink (DL) signals from distant base stations (BSs) propagate over long distances through tropospheric ducting, severely disrupting uplink (UL) reception at local BSs. To address this challenge, we analyze the effect of RI on network performance, including the channel estimation phase. We then develop a solution that identifies the angle-of-arrival (AOA) estimation of RI and designs precoders and combiners that mitigate RI. Our approach employs interference cancellation techniques through null precoding and fractional programming which enhance the performance of the network. Interestingly, we show that using our scheme, uplink communication is possible at low transmit power regimes that were unusable due to RI. Our results further show a 5.23~dB reduction in normalized mean square error for channel estimation and achieved data rates around 5.8~bit/s/Hz at the previously unusable low uplink transmit power conditions.

Remote Interference Mitigation through Null Precoding and Fractional Programming

TL;DR

This work tackles remote interference from tropospheric ducting, which can disrupt uplink reception and channel estimation across distant cells. It develops an active RI mitigation framework combining RI-aware CE/UL processing with two precoding strategies: null precoding to suppress the LoS RI component and fractional-programming (FP) based precoding to balance victim UL performance with aggressor DL service, aided by root-MUSIC AoA estimation of RI. The results show substantial improvements in channel estimation NMSE (e.g., a reduction of about dB) and uplink spectral efficiency (up to about bit/s/Hz at low UL power), while preserving aggressor DL performance within acceptable limits. These findings demonstrate the feasibility of operating RI-affected networks at lower UL transmit powers and suggest FP as a robust method for RI suppression with manageable coordination requirements in large-scale deployments.

Abstract

With the rapid deployment of 5G systems, remote interference (RI) caused by atmospheric ducting has emerged as an occasional, but critical challenge. This phenomenon occurs when the downlink (DL) signals from distant base stations (BSs) propagate over long distances through tropospheric ducting, severely disrupting uplink (UL) reception at local BSs. To address this challenge, we analyze the effect of RI on network performance, including the channel estimation phase. We then develop a solution that identifies the angle-of-arrival (AOA) estimation of RI and designs precoders and combiners that mitigate RI. Our approach employs interference cancellation techniques through null precoding and fractional programming which enhance the performance of the network. Interestingly, we show that using our scheme, uplink communication is possible at low transmit power regimes that were unusable due to RI. Our results further show a 5.23~dB reduction in normalized mean square error for channel estimation and achieved data rates around 5.8~bit/s/Hz at the previously unusable low uplink transmit power conditions.

Paper Structure

This paper contains 9 sections, 20 equations, 5 figures, 1 algorithm.

Figures (5)

  • Figure 1: Two cellular systems suffering from remote interference.
  • Figure 2: Time View
  • Figure 3: NMSE vs Transmit Power Comparison in CE phase
  • Figure 4: UL AR vs Transmit Power Comparison
  • Figure 5: DL AR vs Iteration Time with FP Precoder