⚡ electrical engineering

Leveraging Slowly Time-Varying AP-AP Channels for Interference Mitigation in Dynamic TDD

This paper proposes a method to mitigate cross-link interference in dynamic TDD systems by exploiting the slow time-variation of AP-AP channels to jointly estimate uplink data and interference channels, thereby achieving substantial performance gains over baseline algorithms.

Original authors: Martin Andersson, Tung T. Vu, Pål Frenger, Jan Åslund, Erik G. Larsson

Published 2026-09-11

📖 7 min read🧠 Deep dive

CC BY 4.0

Original authors: Martin Andersson, Tung T. Vu, Pål Frenger, Jan Åslund, Erik G. Larsson

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Modern wireless networks rely on a clever trick to send information back and forth without the signals crashing into each other. Imagine a busy two-way street where cars can only travel in one direction at a time; to keep traffic flowing, the lights switch between allowing northbound traffic and then southbound traffic. In cellular networks, this switching happens so fast that it feels instantaneous to a user, but it is a strict rule: a cell tower either sends data to a phone or listens for data from a phone, never both at the exact same moment on the same frequency. This method, known as time-division duplexing, has been the standard for decades. However, as our demand for data grows, engineers are exploring a more flexible version where each tower decides its own direction moment by moment, based on who needs to send or receive data right now. This dynamic approach promises to handle uneven traffic loads much better, but it introduces a new problem: if one tower is shouting down the line while its neighbor is trying to listen, the shouting drowns out the whispering.

This specific type of noise, where one tower interferes with another, has long been a barrier to making these flexible networks work. The usual solution involves the towers coordinating their schedules or using complex hardware to cancel out the noise, but these methods often require stopping data transmission to measure the interference or demand expensive equipment that is not yet practical for widespread use. A team of researchers has now proposed a different way to handle this noise, one that turns a physical characteristic of the environment into a solution. They realized that while the connection between a moving phone and a tower changes rapidly as the phone moves, the connection between two stationary towers is remarkably stable. It is like the difference between the wind shifting around a runner and the steady breeze between two mountains; the latter barely changes over time. By exploiting this stability, the researchers found a way to listen to the interference and subtract it out without ever needing to stop the data flow to take measurements.

The core of their discovery lies in how the towers communicate with each other. In a standard setup, a tower trying to listen to a phone would be confused by the signal coming from a neighboring tower that is currently sending data. Usually, the listening tower would need to ask the sending tower to pause and send a specific test signal so it could figure out exactly how the interference travels. This pause wastes valuable time and reduces the speed of the network. The new method skips this step entirely. Because the sending tower already knows exactly what data it is transmitting, and because the path between the two towers changes so slowly, the listening tower can treat the incoming data stream from the neighbor as a known reference. It effectively uses the neighbor's own data as a guide to figure out how that data is being distorted by the air between the towers.

To make this work, the researchers developed a mathematical approach that looks at a sequence of time intervals rather than just a single moment. They treat the problem as a puzzle where they must figure out two things at once: the original message sent by the phones and the specific way the interference traveled from the neighbor tower. Because the interference path stays constant for a long time, the system can gather many different snapshots of the same interference path, each carrying a different piece of the puzzle. By combining these snapshots, the system can solve for the interference pattern with high precision, even though the individual pieces of data from the phones are constantly changing. This allows the listening tower to reconstruct the interference signal perfectly and subtract it from what it hears, leaving only the clear message from the phones.

The researchers tested this idea through detailed computer simulations to see how well it would perform in a realistic environment. They modeled a scenario with two towers, each equipped with thirty-two antennas, serving sixteen phones each. The simulations showed that when the interference path between the towers remained stable for a sufficient number of time intervals, the new method could almost completely eliminate the noise. In the best cases, where the interference path changed very slowly, the performance was nearly identical to a hypothetical "perfect" system where the interference path was already known in advance. This is a significant finding because it suggests that the network does not need to sacrifice data transmission time to measure the interference. The method works independently of how strong the interfering signal is, meaning it remains effective whether the neighbor tower is shouting loudly or whispering softly.

However, the researchers also identified a limitation that depends on how quickly the interference path changes. If the path between the towers shifts as fast as the connection to the phones, the system cannot gather enough consistent snapshots to solve the puzzle, and the method fails to find a unique answer. In these faster-changing scenarios, the researchers proposed a workaround: the listening tower simply asks a small group of phones to stay silent for a few moments. This creates a gap in the data that allows the system to isolate the interference and calculate it correctly. While this does reduce the total amount of data sent during those moments, it still provides a much clearer signal than doing nothing at all. The simulations confirmed that even with this compromise, the new method significantly outperforms existing techniques that try to cancel noise by simply ignoring the interference or using standard cancellation methods that require more antennas than are available.

The study also looked at the practical side of implementing this solution. They calculated that the delay introduced by waiting to process multiple time intervals together is acceptable for most services, such as video streaming or web browsing, which can tolerate a slight lag. The amount of data the towers need to share with each other to make this work is also manageable, provided they have a fast connection between them. The researchers noted that their approach does not require any new hardware or changes to the phones themselves; it is purely a change in how the towers process the signals they already receive. This makes it a viable upgrade for existing networks that want to move toward more flexible, dynamic operation without the cost of installing new full-duplex equipment.

Ultimately, the work demonstrates that the physical nature of the environment can be turned into an advantage. By recognizing that the space between towers is more stable than the space between a tower and a moving phone, the researchers found a way to turn a major source of noise into a known quantity. Their simulations suggest that this approach could allow future wireless networks to operate with much higher efficiency, handling the complex, shifting demands of modern data usage without the interference that currently limits their speed. While the method relies on the interference path staying relatively steady, the researchers showed that even when it does not, a simple adjustment can keep the system working effectively, offering a robust path forward for the next generation of mobile networks.

Technical Summary: Leveraging Slowly Time-Varying AP-AP Channels for Interference Mitigation in Dynamic TDD

Problem Statement
The paper addresses the challenge of cross-link interference in dynamic Time-Division Duplexing (TDD) systems, specifically focusing on Access Point-to-Access Point (AP-AP) interference (AAI). In dynamic TDD, individual APs adjust their uplink (UL) and downlink (DL) allocations based on instantaneous traffic demands. While this improves spectral efficiency compared to static TDD, it introduces severe interference when neighboring APs operate in opposite directions (e.g., one transmitting DL while another receives UL) on the same time-frequency resources.

Existing mitigation strategies typically rely on strict coordination of scheduling, beamforming, or power control. A significant limitation of coordinated beamforming approaches is their dependence on precise, frequently updated AP-AP channel state information (CSI). Acquiring this CSI requires pilot transmission between APs, which incurs substantial overhead, reduces the pre-log factor for data transmission, and imposes strict hardware requirements. Furthermore, many existing methods require adapting DL transmissions (e.g., via beamforming) to suppress interference, which wastes degrees of freedom and impairs DL data rates.

Methodology
The authors propose a novel strategy that exploits the physical characteristic that AP-AP channels vary much more slowly over time than User Equipment-to-AP (UE-AP) channels. This is because APs are static while UEs are mobile. The core idea is to treat the known DL transmit signals from interfering APs as "implicit pilots" to estimate the AP-AP channel without dedicated training overhead.

The methodology formulates the AAI mitigation problem as a joint estimation of the UL user data and the AP-AP channel over multiple UE-AP coherence intervals ( $T$ ), during which the AP-AP channel remains constant. Mathematically, this is posed as a coupled least-squares problem:
$\min_{\{X_t\}, H} \sum_{t \in \mathcal{T}} \| Y_t - G_t X_t - H V_t S_t \|^2$
where $Y_t$ is the received signal, $G_t$ is the known UE-AP channel, $X_t$ is the unknown UL data, $V_t S_t$ is the known DL transmit signal, and $H$ is the unknown AP-AP channel.

The paper analyzes the solvability of this problem by examining the rank of the regression matrix.

Full-Rank Case: If the number of coherence intervals $T$ is sufficiently large, the regression matrix has full column rank, allowing for a unique solution via standard least-squares estimation.
Rank-Deficient Case: If $T$ is small (specifically $T=1$ ), the matrix is rank-deficient, and the solution is not unique. To resolve this, the authors propose a modification where a subset of UL data samples is silenced (set to zero) in each coherence interval. This reduces the dimension of the null space, rendering the problem uniquely solvable.

Two algorithms are presented for the rank-deficient case: a joint estimation approach and a sequential approach (channel estimation followed by data detection), which are proven to yield identical solutions.

Key Contributions

Joint Estimation Framework: The paper formulates AAI mitigation as a joint least-squares estimation of UL data and the AP-AP channel, leveraging the slow time-variation of the interference channel and the knowledge of DL transmit signals to eliminate pilot overhead.
Solvability Analysis: The authors provide a rigorous mathematical analysis of the conditions required for a unique solution. They derive necessary conditions on the number of coherence intervals $T$ and matrix dimensions for the regression matrix to be full rank. Specifically, they prove that for $T=1$ , the problem is never identifiable, and establish bounds for $T$ in scenarios with equal numbers of UL/DL antennas and UEs.
Rank-Deficient Remediation: A method is proposed to transform the rank-deficient problem into a uniquely solvable one by sacrificing a subset of UL data samples, ensuring applicability even when the AP-AP channel does not vary slowly enough to support large $T$ .
Performance Independence: The proposed methods are shown to be independent of the DL Signal-to-Noise Ratio (SNR). While higher DL SNR improves channel estimation, it also increases interference power; these effects mathematically cancel out in the covariance of the data estimates.

Results
Numerical simulations demonstrate the efficacy of the proposed methods:

Mean-Square Error (MSE): The proposed method for the full-rank case significantly outperforms baselines without interference mitigation. As $T$ increases (indicating slower channel variation), the MSE approaches that of a "genie-aided" baseline with perfect AAI mitigation. For instance, at $T=20$ , the gap to the genie baseline is less than 2 dB, while the gain over no mitigation is approximately 18 dB.
Spectral Efficiency (SE): The proposed full-rank method achieves SE comparable to the genie-aided baseline for large $T$ . It substantially outperforms a benchmark zero-forcing combining technique, particularly when the number of AP antennas is not significantly larger than the number of UEs. The zero-forcing benchmark suffers because it sacrifices spatial degrees of freedom for interference cancellation and incurs pilot overhead.
Robustness: The methods remain effective even when the strict independence assumptions of channel elements are relaxed (e.g., correlated fading), though the condition number of the regression matrix increases.

Significance and Claims
The paper claims that its proposed technique offers a practical and efficient solution for AAI mitigation in dynamic TDD systems without the heavy overhead associated with traditional coordinated beamforming or pilot-based channel estimation. By leveraging the inherent slow variation of AP-AP channels, the method achieves near-optimal interference cancellation (approaching genie-aided performance) when channels vary very slowly.

The authors emphasize that their approach is a comprehensive extension of their prior conference work, moving from single-slot processing to multi-interval processing. They acknowledge limitations, noting that the exact rank of the regression matrix in the general case remains an open question and that their current framework relies on a strict block-fading assumption. Future work directions identified include developing recursive online variants to reduce latency and extending the method to handle continuous channel variations within coherence intervals. The paper concludes that slowly time-varying AP-AP channels can be effectively leveraged to enhance interference mitigation, offering substantial gains over existing baseline algorithms.

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