⚡ electrical engineering

HAPS-Complemented Terrestrial Networks

This paper proposes a high-altitude platform station (HAPS) relay system operating in full-duplex mode to enhance the downlink capacity of cell-edge users in urban multicell MIMO networks by mitigating interference and path loss through a centralized beamforming algorithm based on successive convex approximation and alternating optimization.

Original authors: Animesh Yadav, Halim Yanikomeroglu

Published 2026-07-15

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CC BY 4.0

Original authors: Animesh Yadav, Halim Yanikomeroglu

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 a bustling city where the internet is like a giant, invisible radio wave trying to reach everyone's phone. Usually, the people living right next to the cell tower (the "near" users) get a super-fast, clear signal. But the folks living on the very edge of the neighborhood (the "edge" users) are stuck with a terrible connection. Why? Because tall skyscrapers and concrete buildings block the signal, creating deep shadows where the radio waves can't reach, and the signals from neighboring towers get messy and interfere with each other.

The authors of this paper asked a simple question: What if we could fly a helper above all those buildings?

The Sky-High Relay

Instead of just relying on towers on the ground, the researchers proposed adding a High-Altitude Platform Station (HAPS). Think of this as a giant, solar-powered drone or airship hovering 22 km (about 13.6 miles) up in the sky. It's so high that it can see over all the skyscrapers, creating a clear, straight-line path (called a "Line-of-Sight" or LoS link) to the ground.

Here is the clever part: This sky-helper doesn't just broadcast; it acts as a relay.

The ground towers send the data meant for the struggling edge-users up to the HAPS.
The HAPS catches it and immediately beams it back down to the edge-users.

Because the HAPS is so high, it avoids the "shadowing" and blockage that plague the ground. Plus, because the HAPS is huge, it can separate its "listening" antennas from its "speaking" antennas, allowing it to talk and listen at the same time without getting confused by its own voice (a trick called full-duplex mode).

The "Traffic Cop" Algorithm

Getting all these signals to work together without causing a chaotic mess is hard. The ground towers and the sky-helper have to coordinate perfectly. The authors created a mathematical "traffic cop" (an algorithm) that figures out exactly how to aim the antennas at the ground towers and the sky-helper.

They had to solve a tricky puzzle: How do we maximize the total speed for everyone while making sure the edge-users get a minimum speed they need to function? They used a method called Successive Convex Approximation (a fancy way of saying they broke the impossible problem into smaller, solvable steps) to find the best settings.

What the Simulations Showed

The researchers didn't just guess; they ran computer simulations to see if this idea actually works. They set up a virtual city with 28 GHz signals (a very fast frequency) and tested it with different city sizes.

Here is what their simulations revealed:

The Sky-Help Wins: In these simulations, the system with the HAPS (Scheme-1) gave the edge-users nearly double the speed compared to a system that only used ground towers (Scheme-2).
Why it works: The ground-only systems struggled because the buildings blocked the signal. The HAPS system bypassed the buildings entirely.
The "Near" User Effect: Interestingly, as the city got bigger (from 100 m to 1,500 m radius), the total speed for the whole system went up. This is because when the city is larger, the "near" users are further away from the sky-helper, so the helper's beam doesn't accidentally bother them as much. However, the speed for the edge-users stayed pretty steady, no matter how big the city got, because the sky-helper's clear path to them remained strong.

What They Didn't Say

It's important to remember what this paper didn't do. The authors did not build a real flying drone and test it in a real city. They did not prove this works in the real world yet; they only showed that it works in their computer simulations. They also didn't test this with satellites or other types of flying vehicles; they focused specifically on this HAPS setup.

The Bottom Line

The paper suggests that if we can get these high-altitude helpers into the sky, we could solve the "dead zone" problem for people on the edge of our cities. By using a smart algorithm to coordinate the ground towers and the sky-helper, we could make sure everyone, even those far from the tower, gets a fast, reliable connection. It's a promising idea for the future of wireless networks, but for now, it remains a very strong simulation rather than a real-world fact.

Technical Summary: HAPS-Complemented Terrestrial Networks for Cell-Edge Capacity Enhancement

Problem Statement
In urban multicell multiple-input multiple-output (MIMO) downlink systems, cell-edge user equipments (UEs) suffer from significantly lower data rates compared to near UEs. This performance degradation is driven by severe shadowing, high path loss, and inter-cell interference (ICI). While terrestrial solutions such as base station (BS) coordination, power control, and cell-free MIMO exist, they often struggle under non-line-of-sight (NLoS) conditions and heavy blockage common in dense urban environments. The paper addresses the challenge of improving the capacity of these edge UEs by integrating a High-Altitude Platform Station (HAPS) into the terrestrial network to act as a relay.

System Model and Methodology
The proposed system consists of $B$ terrestrial cells, each served by a multi-antenna BS operating in half-duplex mode. A full-duplex (FD) HAPS, positioned at an altitude of 22 km with separate transmit and receive antenna arrays, covers all cells. The HAPS exploits its large physical separation between transmit and receive antennas to achieve passive self-interference (SI) suppression, allowing it to operate in full-duplex mode.

The communication protocol operates as follows:

Uplink to HAPS: Terrestrial BSs forward data intended for edge UEs to the FD-HAPS. The BSs employ hybrid beamforming (combining RF and baseband precoders) and Non-Orthogonal Multiple Access (NOMA) to serve both near and edge UEs simultaneously.
HAPS Relay: The FD-HAPS receives signals from all BSs, applies hybrid combining, and uses Successive Interference Cancellation (SIC) to decode the edge-UE data streams.
Downlink to UEs: The HAPS re-transmits the decoded edge-UE data to the intended UEs via line-of-sight (LoS) links, which are less susceptible to urban shadowing than terrestrial links. Near UEs receive signals directly from their serving BS and the HAPS, applying SIC to decode their own data after removing the edge-UE interference.

Optimization and Algorithm
The core technical contribution is the formulation of a joint optimization problem to maximize the achievable sum rate of the system. The optimization variables include the hybrid precoders at the BSs ( $F_{RF}, F_{BB}$ ), the hybrid precoders at the HAPS ( $G_{RF}, G_{BB}$ ), and the hybrid combiners at the HAPS ( $W_{RF}, W_{BB}$ ). The problem is subject to:

Total transmit power constraints for both BSs and the HAPS.
Minimum Quality-of-Service (QoS) rate constraints for edge UEs.
Constant modulus constraints on RF precoders/combiners.

The resulting optimization problem is non-convex due to the objective function and the constraints. To solve this, the authors propose a centralized algorithm based on Successive Convex Approximation (SCA) and Alternating Optimization (AO):

RF Design Simplification: The RF precoders and combiners are initially designed using the conjugate channel phase (for near UEs) or steering vectors (for edge UEs) to simplify the problem.
Equivalent Transformation: Slack variables are introduced to transform the non-convex rate constraints into a form suitable for convex optimization.
Iterative Solution: The problem is decomposed into two subproblems: optimizing the baseband precoders ( $F_{BB}, G_{BB}$ ) and the baseband combiner ( $W_{BB}$ ). Non-convex constraints involving signal-to-interference-plus-noise ratio (SINR) terms are approximated using first-order Taylor expansions.
Convergence: The algorithm alternates between solving these subproblems (formulated as Second-Order Cone Programs, SOCPs) until convergence to a stationary solution.

Key Results
Numerical simulations were conducted in an urban environment with skyscrapers causing heavy shadowing, comparing the proposed scheme (Scheme-1) against two benchmarks: a coordinated terrestrial network without HAPS (Scheme-2) and an uncoordinated terrestrial network (Scheme-3).

Edge UE Performance: The proposed HAPS-assisted scheme significantly outperforms terrestrial-only baselines. Specifically, the sum rate of cell-edge UEs nearly doubles compared to the coordinated terrestrial scheme (Scheme-2). This gain is attributed to the HAPS providing LoS links that bypass terrestrial shadowing and path loss.
System Sum Rate: The overall system sum rate increases as the terrestrial cell radius grows. This is because larger cell radii reduce the interference the HAPS causes to near UEs.
Robustness: The edge-UE sum rate remains relatively stable across varying cell radii, as the mutual interference between edge UEs is largely independent of the cell size.

Significance and Claims
The paper claims to be the first work specifically aimed at improving cell-edge UE capacity in urban areas using HAPS as a relay within a terrestrial network. The authors emphasize that while HAPS has been studied for remote areas and satellite communication, its integration to mitigate urban ICI and shadowing is a novel application. The study demonstrates that integrating HAPS with terrestrial networks, utilizing full-duplex relaying and hybrid beamforming, offers a viable path to enhancing connectivity for the most vulnerable users in next-generation wireless networks (beyond 5G/6G), aligning with 3GPP Release 17 and 19 visions for Non-Terrestrial Network (NTN) integration. The proposed algorithm provides a computationally efficient method to achieve these gains under realistic power and QoS constraints.