⚡ electrical engineering

OTFS-based Integrated Positioning and Communication Systems with Low-Resolution ADCs

This paper proposes an OTFS-based integrated positioning and communication (IPAC) framework that utilizes uplink positioning signal estimation to enhance downlink beamforming, while analyzing the performance degradation caused by low-resolution ADCs.

Original authors: Yueyi Yang, Zeping Sui, Zilong Liu, Leila Musavian

Published 2026-02-10

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CC BY 4.0

Original authors: Yueyi Yang, Zeping Sui, Zilong Liu, Leila Musavian

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

Imagine you are trying to have a high-speed conversation with a friend while riding on a fast-moving rollercoaster, all while trying to use your phone’s GPS to figure out exactly where you are on the track.

This paper tackles a very modern problem: How can we make wireless signals do two jobs at once (talking and locating) even when the hardware is "cheap" and "imperfect"?

Here is the breakdown of the paper using everyday analogies.

1. The Problem: The "Blurry Vision" Dilemma

In high-tech wireless systems (like self-driving cars), we want two things:

Communication: Sending massive amounts of data (the "conversation").
Positioning: Knowing exactly where the device is (the "GPS").

To save money and battery life, engineers want to use Low-Resolution ADCs.

The Analogy: Think of an ADC (Analog-to-Digital Converter) as a camera sensor. A high-resolution ADC is like a professional DSLR camera that captures every tiny detail. A low-resolution ADC is like an old, grainy webcam from 1995. It works, but the image is "pixelated" and "blurry."

When the signal is "blurry" due to these cheap sensors, it becomes very hard to tell exactly how fast a car is moving or exactly where it is located.

2. The Waveform: OTFS (The "Steady Canvas")

Most current wireless tech (like 4G/5G) uses a method called OFDM. However, when things move very fast (high Doppler effect), OFDM gets "smudged," like trying to draw a picture on a moving train.

The researchers use OTFS (Orthogonal Time-Frequency Space).

The Analogy: If OFDM is like trying to draw on a moving train, OTFS is like drawing on a steady canvas that moves with the train. Instead of looking at the signal in terms of "time" and "frequency" (which are constantly changing), OTFS looks at the signal in terms of "delay" and "speed." This makes the signal much more stable and easier to read, even at high speeds.

3. The Solution: The Two-Phase Dance

The paper proposes a two-step system:

Phase 1: The Uplink (The "Scouting Mission")
The user sends a signal up to the base station. The base station uses clever math (called SS-MUSIC) to act like a high-powered telescope. Even though the "image" is grainy (low-resolution), the math helps "de-blur" the signal to figure out:

The angle the signal is coming from.
How much delay there is.
How fast the user is moving.

Phase 2: The Downlink (The "Precision Beam")
Now that the base station knows exactly where the user is and how they are moving, it doesn't just blast signal everywhere. It uses Beamforming.

The Analogy: Instead of using a lightbulb that shines light everywhere in a room, the base station uses a high-end flashlight. Because it did the "scouting mission" in Phase 1, it knows exactly where to point the beam so the user gets a crystal-clear "conversation" without wasting energy.

4. The Results: What did they find?

The researchers ran simulations to see how much the "grainy camera" (low-resolution ADC) hurt the system. They found:

The "Floor" Effect: If you use a very cheap sensor, there is a limit to how good you can get. No matter how much you increase the signal strength, the "pixelation" (quantization noise) creates a permanent blur that you can't get rid of.
The Domino Effect: If your "scouting mission" (positioning) is bad because of the grainy sensor, your "flashlight" (communication beam) will be pointed in the wrong direction, making your internet connection much worse.
The Sweet Spot: They found that even with a relatively low-quality sensor (like a 5-bit ADC), you can still get surprisingly good results if you use their mathematical tricks.

Summary in one sentence:

The paper proves that by using a smarter way of "drawing" signals (OTFS) and clever math to "de-blur" grainy data, we can build cheap, battery-efficient devices that can both talk and navigate perfectly, even at high speeds.

Technical Summary: OTFS-based Integrated Positioning and Communication Systems with Low-Resolution ADCs

1. Problem Statement

As next-generation wireless networks move toward high-mobility scenarios (e.g., autonomous driving, UAVs), there is a critical need for Integrated Positioning and Communication (IPAC) systems. While Orthogonal Time-Frequency Space (OTFS) modulation is a superior waveform for high-mobility environments due to its resilience to Doppler shifts, existing research largely assumes ideal hardware.

In practical large-scale MIMO systems, high-resolution Analog-to-Digital Converters (ADCs) are prohibitively expensive and power-hungry. This paper addresses the challenge of designing an OTFS-based IPAC system that operates under the constraints of low-resolution ADCs, which introduce non-linear quantization noise and signal power degradation, thereby impacting both positioning accuracy and communication reliability.

2. Methodology

The authors propose a two-phase Time Division Duplexing (TDD) framework:

Uplink Phase (Positioning & Channel Estimation):
- The user transmits OTFS positioning signals to the Base Station (BS).
- AoA Estimation: To combat multi-path correlation, the authors employ the Spatial Smoothing-MUSIC (SS-MUSIC) algorithm, which partitions the antenna array into overlapping subarrays to decorrelate signals.
- Parameter Estimation: An iterative interference cancellation scheme is used to estimate the remaining Delay-Doppler parameters. It works by detecting one path at a time and subtracting its contribution from the residual signal to estimate subsequent paths.
- Modeling: The quantization effect is modeled using the Additive Quantization Noise Model (AQNM), which represents the ADC output as a scaled version of the input plus an additive distortion term.
Downlink Phase (Communication):
- The BS utilizes the channel parameters (delay, Doppler, and AoA) estimated during the uplink to perform Zero-Forcing (ZF) beamforming.
- This pre-equalization strategy aims to compensate for channel distortions before transmitting data to the user, thereby improving the Bit Error Rate (BER).
Theoretical Analysis:
- The authors derive the Cramer-Rao Lower Bounds (CRLBs) for the channel parameters and the user's physical position by exploiting the geometric relationship between the delay, Doppler, and Angle-of-Arrival (AoA).

3. Key Contributions

Integrated Framework: Development of a complete OTFS-IPAC system architecture specifically designed for the Delay-Doppler-Angular (DDA) domain under low-resolution ADC constraints.
Algorithm Design: Implementation of a robust SS-MUSIC and iterative interference cancellation pipeline to handle the non-linearities and multi-path effects in the DDA domain.
Theoretical Benchmarking: Derivation of the CRLBs for both channel parameters and user position, providing a fundamental limit for any unbiased estimator in this specific hardware configuration.
Cross-Phase Optimization: Demonstrating how uplink positioning accuracy directly facilitates improved downlink communication through informed beamforming.

4. Results

Estimation Performance: Simulation results show that at low SNR (< 20 dB), channel noise dominates, and ADC resolution has little impact. However, at high SNR (> 20 dB), low-resolution ADCs induce error floors in the Mean Square Error (MSE) for position, Doppler, and channel gains.
Quantization Limits: The CRLBs for low-resolution cases (e.g., 5-bit) plateau at high SNR, revealing the fundamental performance limit imposed by quantization distortion.
Communication Performance: The downlink BER is significantly affected by ADC resolution. A target BER of $10^{-4}$ requires approximately 17 dB more SNR for a 4-bit ADC compared to an infinite-resolution ADC.
Coupling Effect: The study confirms that the accuracy of uplink positioning is a prerequisite for effective downlink beamforming; poor estimation in the uplink leads to a mismatch in the beamforming matrix, causing BER saturation.

5. Significance

This paper provides a realistic assessment of OTFS-based IPAC systems by moving beyond the "ideal hardware" assumption. It highlights the critical trade-off between energy efficiency (achieved via low-resolution ADCs) and system performance (positioning accuracy and data rates). The findings are highly relevant for the design of future 6G hardware, suggesting that a 5-bit ADC may offer a viable balance for high-performance positioning and communication in high-mobility scenarios.