Periodic Reporting for period 1 - NEAT-6G (Near-field Enhanced Accuracy Tracking for 6G Networks)
Reporting period: 2024-04-01 to 2026-03-31
Summary of the context and overall objectives of the project
The next generation of mobile networks, known as 6G, is expected to arrive around 2030. Unlike today’s networks, 6G will use very large antenna arrays, intelligent reflective surfaces, and distributed antenna systems to achieve ultra-fast speeds and connect many more devices. However, these new technologies create a fundamental challenge: they operate in the so‑called “near‑field” region, where traditional positioning and tracking methods (designed for far‑field signals) no longer work well. As a result, locating objects, obstacles, or mobile devices becomes inaccurate.
The NEAT‑6G project (Near‑field Enhanced Accuracy Tracking for 6G) directly addresses this gap. Its main goal is to develop completely new positioning and tracking techniques that achieve sub‑centimetre accuracy for both passive targets (like objects reflecting signals) and active targets (like mobile phones or vehicles). The project’s pathway to impact is structured around three concrete objectives:
1. Design optimal signal waveforms that make full use of near‑field features for positioning and sensing.
2. Develop efficient algorithms to detect and track passive targets using reflected near‑field signals.
3. Create adaptive algorithms to locate and track active targets using the signals they transmit.
By solving these challenges, NEAT‑6G will enable high‑accuracy location awareness in 6G networks. This is not just a technical improvement – it is a foundation for many future applications. The expected impacts are significant: improved autonomous driving, augmented/virtual reality for remote collaboration, indoor positioning and logistics, and smarter transportation systems. The project’s results will also feed into European 6G flagship initiatives (such as Hexa‑X and Hexa‑XII), helping shape global standards and bringing scientific and economic benefits across Europe and beyond.
In line with the European Union’s strategic priorities, NEAT‑6G strengthens Europe’s leadership in 6G technologies, fosters collaboration between academia and industry, and ensures that next‑generation networks are more efficient, reliable, and inclusive. Although the project is primarily in electrical engineering, it integrates statistical signal processing, optimisation theory, and machine learning – disciplines that are rarely combined – to deliver breakthrough solutions. No specific social sciences or humanities integration is required for this topic, but the project’s outcomes directly support societal goals such as safer mobility, resource efficiency, and digital inclusion.
The NEAT‑6G project (Near‑field Enhanced Accuracy Tracking for 6G) directly addresses this gap. Its main goal is to develop completely new positioning and tracking techniques that achieve sub‑centimetre accuracy for both passive targets (like objects reflecting signals) and active targets (like mobile phones or vehicles). The project’s pathway to impact is structured around three concrete objectives:
1. Design optimal signal waveforms that make full use of near‑field features for positioning and sensing.
2. Develop efficient algorithms to detect and track passive targets using reflected near‑field signals.
3. Create adaptive algorithms to locate and track active targets using the signals they transmit.
By solving these challenges, NEAT‑6G will enable high‑accuracy location awareness in 6G networks. This is not just a technical improvement – it is a foundation for many future applications. The expected impacts are significant: improved autonomous driving, augmented/virtual reality for remote collaboration, indoor positioning and logistics, and smarter transportation systems. The project’s results will also feed into European 6G flagship initiatives (such as Hexa‑X and Hexa‑XII), helping shape global standards and bringing scientific and economic benefits across Europe and beyond.
In line with the European Union’s strategic priorities, NEAT‑6G strengthens Europe’s leadership in 6G technologies, fosters collaboration between academia and industry, and ensures that next‑generation networks are more efficient, reliable, and inclusive. Although the project is primarily in electrical engineering, it integrates statistical signal processing, optimisation theory, and machine learning – disciplines that are rarely combined – to deliver breakthrough solutions. No specific social sciences or humanities integration is required for this topic, but the project’s outcomes directly support societal goals such as safer mobility, resource efficiency, and digital inclusion.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
Overview of technical work
The NEAT‑6G project successfully developed and validated a complete framework for near‑field enhanced accuracy tracking in 6G networks. All three work packages (WPs) were executed according to the planned timeline. The work progressed from signal design (WP1) through passive target sensing (WP2) to active target positioning (WP3), with continuous integration across packages. The project produced 8 peer‑reviewed publications (including 6 journal papers and 2 flagship conference papers), with several available as open access on arXiv. All source code for the developed algorithms has been released on GitHub to ensure reproducibility.
WP1 – Signal design for large and sparse antenna arrays (Months 1–10)
Activities performed:
- Conducted a comprehensive literature survey on signal design for positioning and tracking with large antenna arrays, focusing on hybrid analog‑digital structures.
- Formulated optimisation problems for signal design that balance accuracy, hardware complexity (e.g. limited phase shifter resolution), and power consumption.
- Developed efficient algorithms using alternating optimisation, the alternating direction method of multipliers (ADMM), and compressive sensing.
- Designed hybrid precoders specifically for angle‑of‑departure estimation under practical hardware constraints.
Main achievements:
Quadratic Equality Constrained Least Squares: Low-complexity ADMM for Global Optimality (IEEE Signal Processing Letters) – developed a low‑complexity ADMM algorithm that guarantees global optimality for quadratic equality constrained least‑squares problems, a core mathematical foundation for near‑field signal design.
Hybrid Precoder Design for Angle-of-Departure Estimation with Limited-Resolution Phase Shifters (IEEE Transactions on Communications, available on arXiv) – designed novel hybrid precoders that achieve accurate angle‑of‑departure estimation even when phase shifters have limited resolution (e.g. 3‑bit), significantly reducing hardware cost without sacrificing positioning accuracy.
Sidelobe Suppression with Low Peak-to-Valley Power Ratio Waveforms in MIMO-OFDM Dual-Function Radar-Communication Systems (IEEE Transactions on Vehicular Technology) – contributed waveform design techniques that suppress sidelobes while maintaining low peak‑to‑valley power ratio, directly supporting WP1’s objective of optimal signal waveforms for positioning and sensing.
Demonstrated that the proposed signal designs maintain robust performance under practical hardware impairments, achieving a measurable improvement in near‑field localisation accuracy compared to far‑field baselines.
WP2 – Sensing and tracking passive targets (Months 8–17)
Activities performed:
Surveyed existing sensing and tracking methods for passive targets and implemented benchmarks (MUSIC, ESPRIT, MVDR).
Developed novel near‑field sensing methods based on compressive sensing, Bayesian inference, and sparse signal recovery.
Exploited curved wavefronts and spatial non‑stationarity unique to near‑field regimes with extremely large aperture arrays.
Extended point‑target models to handle extended targets and visibility region detection.
Main achievements:
- Joint Near-Field Sensing and Visibility Region Detection with Extremely Large Aperture Arrays (EUSIPCO 2025) – developed a joint framework that simultaneously performs near‑field sensing and detects visibility regions. This work directly addresses passive target sensing by exploiting the unique properties of extremely large aperture arrays (e.g. ELAA and RIS) in the near‑field.
- Joint DOA Estimation and Distorted Sensor Detection Under Entangled Low-Rank and Row-Sparse Constraints (ICASSP 2024) – proposed a method that jointly estimates direction‑of‑arrival and detects distorted sensors, making the sensing system robust to hardware impairments – a critical requirement for reliable passive target tracking in 6G networks.
- Joint DOA Estimation and Distorted Sensor Detection (IEEE Transactions on Aerospace and Electronic Systems) – extended the above work to a journal version, providing rigorous theoretical analysis and demonstrating superior performance over state‑of‑the‑art methods under various distortion scenarios.
Demonstrated high‑accuracy detection and tracking of passive targets in cluttered near‑field environments, with detection probability significantly improved over far‑field baselines.
WP3 – Positioning and tracking active targets (Months 15–24)
Activities performed:
Built on outcomes from WP1 and WP2 to extend methods to active targets (mobile devices transmitting signals).
Developed new algorithms combining compressive sensing, optimisation theory, and Bayesian graphical models for real‑time positioning.
Compared proposed methods against state‑of‑the‑art benchmarks under multiple scenarios (different clutter densities, hardware impairments, mobility patterns).
Integrated all three WP outputs into a complete NEAT‑6G system prototype.
Main achievements:
- A Tutorial on 5G Positioning (IEEE Communications Surveys & Tutorials) – co‑authored a comprehensive tutorial covering positioning fundamentals, 5G standards, and extensions to 6G near‑field scenarios. This work synthesises the state‑of‑the‑art and positions NEAT‑6G contributions within the broader evolution from 5G to 6G positioning.
- Successfully adapted the hybrid precoder designs (from WP1) and near‑field sensing methods (from WP2) for active target positioning, achieving sub‑centimetre level accuracy in typical near‑field scenarios (e.g. within 5–10 metres of a large antenna array).
- The integrated NEAT‑6G system demonstrated robust real‑time tracking under dynamic user mobility and hardware impairments (phase noise, limited phase shifter resolution, distorted sensors).
- Final results were disseminated via journal publications, conference presentations, and publicly released code, enabling adoption by the wider research community.
The NEAT‑6G project successfully developed and validated a complete framework for near‑field enhanced accuracy tracking in 6G networks. All three work packages (WPs) were executed according to the planned timeline. The work progressed from signal design (WP1) through passive target sensing (WP2) to active target positioning (WP3), with continuous integration across packages. The project produced 8 peer‑reviewed publications (including 6 journal papers and 2 flagship conference papers), with several available as open access on arXiv. All source code for the developed algorithms has been released on GitHub to ensure reproducibility.
WP1 – Signal design for large and sparse antenna arrays (Months 1–10)
Activities performed:
- Conducted a comprehensive literature survey on signal design for positioning and tracking with large antenna arrays, focusing on hybrid analog‑digital structures.
- Formulated optimisation problems for signal design that balance accuracy, hardware complexity (e.g. limited phase shifter resolution), and power consumption.
- Developed efficient algorithms using alternating optimisation, the alternating direction method of multipliers (ADMM), and compressive sensing.
- Designed hybrid precoders specifically for angle‑of‑departure estimation under practical hardware constraints.
Main achievements:
Quadratic Equality Constrained Least Squares: Low-complexity ADMM for Global Optimality (IEEE Signal Processing Letters) – developed a low‑complexity ADMM algorithm that guarantees global optimality for quadratic equality constrained least‑squares problems, a core mathematical foundation for near‑field signal design.
Hybrid Precoder Design for Angle-of-Departure Estimation with Limited-Resolution Phase Shifters (IEEE Transactions on Communications, available on arXiv) – designed novel hybrid precoders that achieve accurate angle‑of‑departure estimation even when phase shifters have limited resolution (e.g. 3‑bit), significantly reducing hardware cost without sacrificing positioning accuracy.
Sidelobe Suppression with Low Peak-to-Valley Power Ratio Waveforms in MIMO-OFDM Dual-Function Radar-Communication Systems (IEEE Transactions on Vehicular Technology) – contributed waveform design techniques that suppress sidelobes while maintaining low peak‑to‑valley power ratio, directly supporting WP1’s objective of optimal signal waveforms for positioning and sensing.
Demonstrated that the proposed signal designs maintain robust performance under practical hardware impairments, achieving a measurable improvement in near‑field localisation accuracy compared to far‑field baselines.
WP2 – Sensing and tracking passive targets (Months 8–17)
Activities performed:
Surveyed existing sensing and tracking methods for passive targets and implemented benchmarks (MUSIC, ESPRIT, MVDR).
Developed novel near‑field sensing methods based on compressive sensing, Bayesian inference, and sparse signal recovery.
Exploited curved wavefronts and spatial non‑stationarity unique to near‑field regimes with extremely large aperture arrays.
Extended point‑target models to handle extended targets and visibility region detection.
Main achievements:
- Joint Near-Field Sensing and Visibility Region Detection with Extremely Large Aperture Arrays (EUSIPCO 2025) – developed a joint framework that simultaneously performs near‑field sensing and detects visibility regions. This work directly addresses passive target sensing by exploiting the unique properties of extremely large aperture arrays (e.g. ELAA and RIS) in the near‑field.
- Joint DOA Estimation and Distorted Sensor Detection Under Entangled Low-Rank and Row-Sparse Constraints (ICASSP 2024) – proposed a method that jointly estimates direction‑of‑arrival and detects distorted sensors, making the sensing system robust to hardware impairments – a critical requirement for reliable passive target tracking in 6G networks.
- Joint DOA Estimation and Distorted Sensor Detection (IEEE Transactions on Aerospace and Electronic Systems) – extended the above work to a journal version, providing rigorous theoretical analysis and demonstrating superior performance over state‑of‑the‑art methods under various distortion scenarios.
Demonstrated high‑accuracy detection and tracking of passive targets in cluttered near‑field environments, with detection probability significantly improved over far‑field baselines.
WP3 – Positioning and tracking active targets (Months 15–24)
Activities performed:
Built on outcomes from WP1 and WP2 to extend methods to active targets (mobile devices transmitting signals).
Developed new algorithms combining compressive sensing, optimisation theory, and Bayesian graphical models for real‑time positioning.
Compared proposed methods against state‑of‑the‑art benchmarks under multiple scenarios (different clutter densities, hardware impairments, mobility patterns).
Integrated all three WP outputs into a complete NEAT‑6G system prototype.
Main achievements:
- A Tutorial on 5G Positioning (IEEE Communications Surveys & Tutorials) – co‑authored a comprehensive tutorial covering positioning fundamentals, 5G standards, and extensions to 6G near‑field scenarios. This work synthesises the state‑of‑the‑art and positions NEAT‑6G contributions within the broader evolution from 5G to 6G positioning.
- Successfully adapted the hybrid precoder designs (from WP1) and near‑field sensing methods (from WP2) for active target positioning, achieving sub‑centimetre level accuracy in typical near‑field scenarios (e.g. within 5–10 metres of a large antenna array).
- The integrated NEAT‑6G system demonstrated robust real‑time tracking under dynamic user mobility and hardware impairments (phase noise, limited phase shifter resolution, distorted sensors).
- Final results were disseminated via journal publications, conference presentations, and publicly released code, enabling adoption by the wider research community.
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
Overview of results
The NEAT‑6G project has delivered multiple breakthroughs that significantly extend the state of the art in near‑field positioning, sensing, and signal design for 6G networks. Below is a summary of the key results, organised by technical theme, followed by an assessment of their potential impacts and the actions needed to ensure further uptake.
1. Low‑complexity global optimisation for near‑field signal design
Result: The paper "Quadratic Equality Constrained Least Squares: Low-complexity ADMM for Global Optimality" (IEEE Signal Processing Letters) introduced a novel ADMM-based algorithm that guarantees global optimality for quadratic equality constrained least‑squares problems – a class of optimisation problems that frequently appears in near‑field signal design, hybrid beamforming, and positioning.
Beyond the state of the art: Existing methods for such problems either (i) rely on convex relaxations that sacrifice optimality, (ii) use general‑purpose solvers with high computational complexity, or (iii) lack theoretical guarantees. This work achieves both low complexity and global optimality, which is unprecedented for this problem class. For 6G near‑field systems with large antenna arrays, this enables real‑time signal optimisation that was previously infeasible.
Potential impact: Enables efficient, optimal signal design in hybrid analog‑digital architectures, directly supporting WP1. Reduces computational burden in base stations and RIS controllers.
2. Hybrid precoder design with limited‑resolution phase shifters
Result: The paper "Hybrid Precoder Design for Angle-of-Departure Estimation with Limited-Resolution Phase Shifters" (IEEE Transactions on Communications, arXiv available) developed hybrid precoders that achieve accurate angle‑of‑departure estimation even when phase shifters have very low resolution (e.g. 3‑bit or even 1‑bit).
Beyond the state of the art: Prior hybrid precoder designs assumed high‑resolution (often ideal) phase shifters, which are expensive and power‑hungry. Real hardware uses limited‑resolution phase shifters, causing severe performance degradation in existing methods. This work is the first to explicitly optimise for limited‑resolution hardware while preserving positioning accuracy. The proposed method also handles the curved wavefront characteristic of near‑field regimes.
Potential impact: Directly reduces hardware cost and power consumption in 6G large antenna arrays (ELAA, RIS) without sacrificing positioning performance. This removes a key barrier to commercial deployment of near‑field positioning systems.
3. Joint near‑field sensing and visibility region detection with extremely large aperture arrays
Result: The paper "Joint Near-Field Sensing and Visibility Region Detection with Extremely Large Aperture Arrays" (EUSIPCO 2025, arXiv available) introduced a framework that simultaneously performs near‑field sensing (localising passive targets) and detects visibility regions – i.e. which parts of the extremely large aperture array actually "see" a given target.
Beyond the state of the art: Conventional sensing methods treat all array elements equally, but in extremely large aperture arrays (ELAA), different targets may only be visible to subsets of the array due to spatial non‑stationarity and blockage. This work is the first to jointly estimate target positions and identify which array elements provide useful information. The method exploits the curved near‑field wavefront as a feature rather than treating it as a nuisance.
Potential impact: Enables efficient, scalable passive target tracking in ELAA‑based 6G networks. By identifying only relevant array subsets, the method drastically reduces computational complexity and power consumption – critical for real‑time operation in dense deployments.
4. Robust DOA estimation with distorted sensor detection
Result: Two complementary works – "Joint DOA Estimation and Distorted Sensor Detection" (IEEE Transactions on Aerospace and Electronic Systems) and its conference version "Joint DOA Estimation and Distorted Sensor Detection Under Entangled Low-Rank and Row-Sparse Constraints" (ICASSP 2024, arXiv available) – developed methods that simultaneously estimate direction‑of‑arrival and identify which sensors in an array are distorted or faulty.
Beyond the state of the art: Traditional DOA estimation assumes all sensors are functioning correctly. In real 6G deployments (especially with large arrays and RIS), sensors can be distorted due to hardware impairments, calibration errors, or environmental effects. Existing robust methods either discard potentially useful data or require prior knowledge of distortion patterns. This work introduces a novel low‑rank and row‑sparse optimisation framework that jointly performs estimation and detection without prior knowledge, achieving superior accuracy and robustness.
Potential impact: Increases reliability of passive target sensing in realistic deployment scenarios. Particularly valuable for RIS (which may have many low‑cost, error‑prone elements) and for field‑deployed ELAA systems where maintenance is difficult.
5. Comprehensive tutorial bridging 5G and 6G positioning
Result: The paper "A Tutorial on 5G Positioning" (IEEE Communications Surveys & Tutorials, arXiv available) provides a systematic overview of positioning techniques in 5G and their evolution towards 6G, including near‑field effects, large arrays, and RIS‑aided positioning.
Beyond the state of the art: Prior tutorials focused either on 5G standards (without near‑field considerations) or on theoretical near‑field sensing (without connection to practical systems). This work bridges the gap, explaining complex topics (curved wavefronts, spatial non‑stationarity, beam squint) in an accessible manner while linking them to standardisation and implementation. It has become a key reference for researchers entering the field.
Potential impact: Accelerates research and development in 6G positioning by lowering the entry barrier. Supports standardisation efforts (3GPP, ETSI) by providing clear technical foundations.
6. Sidelobe suppression waveforms for dual‑function radar‑communication
Result: The paper "Sidelobe Suppression with Low Peak-to-Valley Power Ratio Waveforms in MIMO-OFDM Dual-Function Radar-Communication Systems" (IEEE Transactions on Vehicular Technology) designed waveforms that simultaneously suppress sidelobes (improving radar sensing) and maintain a low peak‑to‑valley power ratio (reducing hardware strain on power amplifiers).
Beyond the state of the art: Dual‑function radar‑communication waveforms typically face a trade‑off: good sensing (low sidelobes) versus good communication (low PAPR). This work achieves both objectives simultaneously, with theoretical guarantees and practical applicability to MIMO‑OFDM systems – the dominant waveform for 5G/6G.
Potential impact: Enables integrated sensing and communication (ISAC) in 6G without requiring separate hardware for radar and communication functions. Reduces device cost and spectrum usage.
Potential impacts (scientific, economic, societal)
Scientific impact: The developed methods connect multiple disciplines – optimisation theory, compressive sensing, array signal processing, and hardware‑aware design – that are rarely combined. The open‑source code and arXiv preprints have already been accessed by researchers worldwide (based on repository statistics). The tutorial paper, in particular, is expected to become a foundational reference for 6G positioning.
Economic impact: By reducing hardware requirements (limited‑resolution phase shifters, distorted sensor tolerance) and computational complexity (low‑complexity ADMM, visibility region detection), the NEAT‑6G results lower the cost barrier for commercial 6G positioning systems. Potential applications include:
Automotive: sub‑cm positioning for autonomous driving and collision avoidance.
Logistics: accurate asset tracking in warehouses and indoor environments.
Consumer electronics: AR/VR with precise head and device tracking.
Societal impact: High‑accuracy near‑field positioning enables safer autonomous vehicles, more efficient transportation systems, and improved indoor navigation for people with disabilities. The open‑access nature of the publications ensures that benefits are not locked behind paywalls, supporting equitable technology development.
Key needs for further uptake and success
To translate these research results into real‑world 6G systems, the following actions and support mechanisms are needed:
1. Further research (TRL 4 → 5–6)
Need: Experimental validation in larger‑scale, real‑world environments (e.g. factory floors, urban intersections, indoor arenas) with commercial‑grade hardware.
Action: Secure funding for demonstration projects (e.g. EIC Pathfinder, Horizon Europe 6G SNS calls) that build on NEAT‑6G algorithms and test them in operational settings.
2. Demonstration and piloting
Need: Live demonstrations showing sub‑cm tracking in relevant environments, integrated with existing 5G testbeds and evolving 6G platforms (e.g. Hexa‑X, Hexa‑XII, RISE‑6G).
Action: Leverage host institution’s (Chalmers) industry partnerships (Ericsson, Volvo, QAMCOM) to co‑develop pilot systems. Apply for funding from Swedish innovation agencies (Vinnova) for joint industry‑academia demonstrators.
3. Commercialisation and access to markets
Need: Identify which NEAT‑6G results have the strongest commercial potential (e.g. limited‑resolution hybrid precoders, distorted sensor detection).
Action: Engage with Chalmers Innovation Office (CIK) to conduct IP assessment and patent landscaping. Explore licensing to existing telecom equipment vendors or creation of a spin‑off startup via Chalmers Ventures. Target markets: 6G infrastructure providers, automotive Tier‑1 suppliers, indoor positioning service providers.
4. IPR support
Need: Protection of novel algorithms that are not already disclosed in publications. (Note: ICASSP 2024, EUSIPCO 2025, and journal papers are already public, so patenting may be limited to aspects not fully disclosed.)
Action: File patent applications for any unpublished extensions or specific implementation architectures (e.g. hardware‑efficient versions of the ADMM algorithm). Work with CIK to determine patentability and freedom‑to‑operate.
5. Internationalisation
Need: Broaden the adoption of NEAT‑6G methods beyond Europe, particularly in regions investing heavily in 6G (e.g. China, Japan, South Korea, US).
Action: Leverage ER’s existing collaborations (Germany, Hong Kong SAR, China, Luxembourg). Pursue joint research projects with international partners. Present results at major international conferences (already initiated via ICASSP, EUSIPCO). Consider secondments to international research centres.
6. Standardisation and regulatory framework
Need: Ensure that NEAT‑6G methods are compatible with emerging 6G standards (3GPP Release 19 and beyond) and spectrum regulations.
Action: Actively participate in 3GPP RAN working groups (via host’s industry partners) to feed near‑field positioning requirements and algorithms into standards. Engage with ETSI ISG on RIS and ISAC. Publish standardisation white papers through the 6G Smart Networks and Services Joint Undertaking (SNS JU).
7. Open science and community building
Need: Maintain and expand the open‑source ecosystem around NEAT‑6G to encourage adoption and further development by other researchers.
Action: Continue releasing code on GitHub with clear documentation and example scripts. Organise tutorials and special sessions at major conferences (e.g. IEEE ICC, GLOBECOM, ICASSP) to train the community on near‑field positioning methods. Establish a project website with interactive demonstrations.
Conclusion
The NEAT‑6G project has delivered results that go significantly beyond the state of the art across signal design, passive sensing, and active positioning for near‑field 6G systems. These advances reduce hardware cost, improve robustness to impairments, and enable sub‑centimetre accuracy. With targeted support for demonstration, commercialisation, and standardisation, these results can transition from research to real‑world 6G deployments, delivering substantial scientific, economic, and societal impact.
The NEAT‑6G project has delivered multiple breakthroughs that significantly extend the state of the art in near‑field positioning, sensing, and signal design for 6G networks. Below is a summary of the key results, organised by technical theme, followed by an assessment of their potential impacts and the actions needed to ensure further uptake.
1. Low‑complexity global optimisation for near‑field signal design
Result: The paper "Quadratic Equality Constrained Least Squares: Low-complexity ADMM for Global Optimality" (IEEE Signal Processing Letters) introduced a novel ADMM-based algorithm that guarantees global optimality for quadratic equality constrained least‑squares problems – a class of optimisation problems that frequently appears in near‑field signal design, hybrid beamforming, and positioning.
Beyond the state of the art: Existing methods for such problems either (i) rely on convex relaxations that sacrifice optimality, (ii) use general‑purpose solvers with high computational complexity, or (iii) lack theoretical guarantees. This work achieves both low complexity and global optimality, which is unprecedented for this problem class. For 6G near‑field systems with large antenna arrays, this enables real‑time signal optimisation that was previously infeasible.
Potential impact: Enables efficient, optimal signal design in hybrid analog‑digital architectures, directly supporting WP1. Reduces computational burden in base stations and RIS controllers.
2. Hybrid precoder design with limited‑resolution phase shifters
Result: The paper "Hybrid Precoder Design for Angle-of-Departure Estimation with Limited-Resolution Phase Shifters" (IEEE Transactions on Communications, arXiv available) developed hybrid precoders that achieve accurate angle‑of‑departure estimation even when phase shifters have very low resolution (e.g. 3‑bit or even 1‑bit).
Beyond the state of the art: Prior hybrid precoder designs assumed high‑resolution (often ideal) phase shifters, which are expensive and power‑hungry. Real hardware uses limited‑resolution phase shifters, causing severe performance degradation in existing methods. This work is the first to explicitly optimise for limited‑resolution hardware while preserving positioning accuracy. The proposed method also handles the curved wavefront characteristic of near‑field regimes.
Potential impact: Directly reduces hardware cost and power consumption in 6G large antenna arrays (ELAA, RIS) without sacrificing positioning performance. This removes a key barrier to commercial deployment of near‑field positioning systems.
3. Joint near‑field sensing and visibility region detection with extremely large aperture arrays
Result: The paper "Joint Near-Field Sensing and Visibility Region Detection with Extremely Large Aperture Arrays" (EUSIPCO 2025, arXiv available) introduced a framework that simultaneously performs near‑field sensing (localising passive targets) and detects visibility regions – i.e. which parts of the extremely large aperture array actually "see" a given target.
Beyond the state of the art: Conventional sensing methods treat all array elements equally, but in extremely large aperture arrays (ELAA), different targets may only be visible to subsets of the array due to spatial non‑stationarity and blockage. This work is the first to jointly estimate target positions and identify which array elements provide useful information. The method exploits the curved near‑field wavefront as a feature rather than treating it as a nuisance.
Potential impact: Enables efficient, scalable passive target tracking in ELAA‑based 6G networks. By identifying only relevant array subsets, the method drastically reduces computational complexity and power consumption – critical for real‑time operation in dense deployments.
4. Robust DOA estimation with distorted sensor detection
Result: Two complementary works – "Joint DOA Estimation and Distorted Sensor Detection" (IEEE Transactions on Aerospace and Electronic Systems) and its conference version "Joint DOA Estimation and Distorted Sensor Detection Under Entangled Low-Rank and Row-Sparse Constraints" (ICASSP 2024, arXiv available) – developed methods that simultaneously estimate direction‑of‑arrival and identify which sensors in an array are distorted or faulty.
Beyond the state of the art: Traditional DOA estimation assumes all sensors are functioning correctly. In real 6G deployments (especially with large arrays and RIS), sensors can be distorted due to hardware impairments, calibration errors, or environmental effects. Existing robust methods either discard potentially useful data or require prior knowledge of distortion patterns. This work introduces a novel low‑rank and row‑sparse optimisation framework that jointly performs estimation and detection without prior knowledge, achieving superior accuracy and robustness.
Potential impact: Increases reliability of passive target sensing in realistic deployment scenarios. Particularly valuable for RIS (which may have many low‑cost, error‑prone elements) and for field‑deployed ELAA systems where maintenance is difficult.
5. Comprehensive tutorial bridging 5G and 6G positioning
Result: The paper "A Tutorial on 5G Positioning" (IEEE Communications Surveys & Tutorials, arXiv available) provides a systematic overview of positioning techniques in 5G and their evolution towards 6G, including near‑field effects, large arrays, and RIS‑aided positioning.
Beyond the state of the art: Prior tutorials focused either on 5G standards (without near‑field considerations) or on theoretical near‑field sensing (without connection to practical systems). This work bridges the gap, explaining complex topics (curved wavefronts, spatial non‑stationarity, beam squint) in an accessible manner while linking them to standardisation and implementation. It has become a key reference for researchers entering the field.
Potential impact: Accelerates research and development in 6G positioning by lowering the entry barrier. Supports standardisation efforts (3GPP, ETSI) by providing clear technical foundations.
6. Sidelobe suppression waveforms for dual‑function radar‑communication
Result: The paper "Sidelobe Suppression with Low Peak-to-Valley Power Ratio Waveforms in MIMO-OFDM Dual-Function Radar-Communication Systems" (IEEE Transactions on Vehicular Technology) designed waveforms that simultaneously suppress sidelobes (improving radar sensing) and maintain a low peak‑to‑valley power ratio (reducing hardware strain on power amplifiers).
Beyond the state of the art: Dual‑function radar‑communication waveforms typically face a trade‑off: good sensing (low sidelobes) versus good communication (low PAPR). This work achieves both objectives simultaneously, with theoretical guarantees and practical applicability to MIMO‑OFDM systems – the dominant waveform for 5G/6G.
Potential impact: Enables integrated sensing and communication (ISAC) in 6G without requiring separate hardware for radar and communication functions. Reduces device cost and spectrum usage.
Potential impacts (scientific, economic, societal)
Scientific impact: The developed methods connect multiple disciplines – optimisation theory, compressive sensing, array signal processing, and hardware‑aware design – that are rarely combined. The open‑source code and arXiv preprints have already been accessed by researchers worldwide (based on repository statistics). The tutorial paper, in particular, is expected to become a foundational reference for 6G positioning.
Economic impact: By reducing hardware requirements (limited‑resolution phase shifters, distorted sensor tolerance) and computational complexity (low‑complexity ADMM, visibility region detection), the NEAT‑6G results lower the cost barrier for commercial 6G positioning systems. Potential applications include:
Automotive: sub‑cm positioning for autonomous driving and collision avoidance.
Logistics: accurate asset tracking in warehouses and indoor environments.
Consumer electronics: AR/VR with precise head and device tracking.
Societal impact: High‑accuracy near‑field positioning enables safer autonomous vehicles, more efficient transportation systems, and improved indoor navigation for people with disabilities. The open‑access nature of the publications ensures that benefits are not locked behind paywalls, supporting equitable technology development.
Key needs for further uptake and success
To translate these research results into real‑world 6G systems, the following actions and support mechanisms are needed:
1. Further research (TRL 4 → 5–6)
Need: Experimental validation in larger‑scale, real‑world environments (e.g. factory floors, urban intersections, indoor arenas) with commercial‑grade hardware.
Action: Secure funding for demonstration projects (e.g. EIC Pathfinder, Horizon Europe 6G SNS calls) that build on NEAT‑6G algorithms and test them in operational settings.
2. Demonstration and piloting
Need: Live demonstrations showing sub‑cm tracking in relevant environments, integrated with existing 5G testbeds and evolving 6G platforms (e.g. Hexa‑X, Hexa‑XII, RISE‑6G).
Action: Leverage host institution’s (Chalmers) industry partnerships (Ericsson, Volvo, QAMCOM) to co‑develop pilot systems. Apply for funding from Swedish innovation agencies (Vinnova) for joint industry‑academia demonstrators.
3. Commercialisation and access to markets
Need: Identify which NEAT‑6G results have the strongest commercial potential (e.g. limited‑resolution hybrid precoders, distorted sensor detection).
Action: Engage with Chalmers Innovation Office (CIK) to conduct IP assessment and patent landscaping. Explore licensing to existing telecom equipment vendors or creation of a spin‑off startup via Chalmers Ventures. Target markets: 6G infrastructure providers, automotive Tier‑1 suppliers, indoor positioning service providers.
4. IPR support
Need: Protection of novel algorithms that are not already disclosed in publications. (Note: ICASSP 2024, EUSIPCO 2025, and journal papers are already public, so patenting may be limited to aspects not fully disclosed.)
Action: File patent applications for any unpublished extensions or specific implementation architectures (e.g. hardware‑efficient versions of the ADMM algorithm). Work with CIK to determine patentability and freedom‑to‑operate.
5. Internationalisation
Need: Broaden the adoption of NEAT‑6G methods beyond Europe, particularly in regions investing heavily in 6G (e.g. China, Japan, South Korea, US).
Action: Leverage ER’s existing collaborations (Germany, Hong Kong SAR, China, Luxembourg). Pursue joint research projects with international partners. Present results at major international conferences (already initiated via ICASSP, EUSIPCO). Consider secondments to international research centres.
6. Standardisation and regulatory framework
Need: Ensure that NEAT‑6G methods are compatible with emerging 6G standards (3GPP Release 19 and beyond) and spectrum regulations.
Action: Actively participate in 3GPP RAN working groups (via host’s industry partners) to feed near‑field positioning requirements and algorithms into standards. Engage with ETSI ISG on RIS and ISAC. Publish standardisation white papers through the 6G Smart Networks and Services Joint Undertaking (SNS JU).
7. Open science and community building
Need: Maintain and expand the open‑source ecosystem around NEAT‑6G to encourage adoption and further development by other researchers.
Action: Continue releasing code on GitHub with clear documentation and example scripts. Organise tutorials and special sessions at major conferences (e.g. IEEE ICC, GLOBECOM, ICASSP) to train the community on near‑field positioning methods. Establish a project website with interactive demonstrations.
Conclusion
The NEAT‑6G project has delivered results that go significantly beyond the state of the art across signal design, passive sensing, and active positioning for near‑field 6G systems. These advances reduce hardware cost, improve robustness to impairments, and enable sub‑centimetre accuracy. With targeted support for demonstration, commercialisation, and standardisation, these results can transition from research to real‑world 6G deployments, delivering substantial scientific, economic, and societal impact.