Non-Orthogonal Multiple Access (NOMA/RSMA)

Multiple access techniques allow multiple users to share network resources in either an orthogonal or non-orthogonal manner, optimizing capacity and minimizing inter-user interference. As the world anticipates the massive number of users and devices in the upcoming 6G networks, the research focus is shifting from traditional orthogonal multiple access (OMA) to advanced non-orthogonal multiple access (NOMA). NOMA facilitates resource sharing among users while managing and utilizing interference instead of completely avoiding it. At the transmitter, efficient resource allocation ensures optimal sharing, while at the receiver, interference management techniques separate users’ signals. This approach makes NOMA highly effective for accommodating the growing demands of next-generation wireless networks. For uplink communication in 6G, code-domain NOMA has emerged as the leading candidate. Meanwhile, for downlink communication, Rate-Splitting Multiple Access (RSMA) is expected to dominate.

Our Research Focus at CoSiNC Lab:

At CoSiNC Lab, we are dedicated to advancing NOMA technologies, with a particular emphasis on enhancing RSMA techniques. Our research aims to address both fundamental challenges and practical applications in the following areas:

1. Efficient Sharing Approaches for NOMA approaches.
2. Reliability and low latency RSMA networks.
3. RSMA for advanced MIMO networks.
4. Physical layer security and privacy for NOMA approaches.
5. NOMA approaches for 5G services such as eMBB, uRLLC, and mMTC.

Radio localization is a technique used to determine the position of an object or device using radio signals. This technology has broad applications in navigation, security, and automation, offering a reliable alternative to vision-based systems, especially in environments with limited visibility and enclosed spaces. From GPS navigation to indoor positioning and autonomous robotics, radio localization plays a crucial role in modern technological advancements.

Radio localization relies on analyzing the characteristics of received radio signals to estimate a target's position. By using localization techniques such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), Received Signal Strength (RSS), Fingerprinting, Angle of Arrival (AoA), and Channel Frequency Response (CFR). Further enhanced by Machine Learning & AI-based Localization.

Applications for Radio Localization:

• Global Navigation Satellite Systems (GNSS): GPS, Galileo, and BeiDou enabled outdoor positioning.
• Indoor Positioning Systems (IPS): Wi-Fi, Bluetooth, and UWB-based methods are used in smart buildings, shopping malls, and hospitals.
• Device-free Localization (DFL): Tracks movement without requiring an active transmitting device, beneficial for security applications.
• Autonomous Vehicles & Robotics: Self-driving cars and drones rely on precise localization for safe navigation.
• Industrial & IoT Applications: Logistics, asset tracking, and smart factories use RFID and UWB for localization.

Challenges & Future Directions

• Multipath Interference: Reflected signals can create localization errors.
• Accuracy vs. Complexity Trade-off: Achieving high precision requires advanced algorithms and increased computational power.
• Integration with AI & Sensor Fusion: Combining radio localization with LiDAR and vision-based systems enhances performance.
• Energy Efficiency: Low-power optimization for IoT applications is crucial for long-term sustainability.

Wireless technology seeks to enable global connectivity through environmentally sustainable networks, improving overall quality of life amid the rapid evolution of the Internet of Things (IoT). As IoT devices are deployed more widely, wireless applications across various areas are increasing. One major challenge is providing these devices with energy-efficient communication within a limited radio spectrum.

Ambient power-enabled IoT (Ambient IoT) is a class of IoT devices that harness ambient energy sources for operation and communication. This approach aims to reduce the reliance on traditional power sources, such as batteries, by utilizing energy available in the environment. Ambient IoT represents a significant advancement in the design and operation of IoT devices, emphasizing energy efficiency and sustainability. By leveraging ambient energy sources, these devices can operate autonomously and reliably in diverse environments, paving the way for innovative applications and enhanced connectivity in the IoT ecosystem. Compared to conventional solutions such as RFID, LoRa, and NB-IoT, Ambient IoT presents a generalized flexible framework for low-power communication, leveraging various device capabilities, including fully passive, semi-passive, and active architectures. Passive Ambient IoT devices use backscatter communication for data transmission, which enables them to transmit information by reflecting existing radio frequency (RF) signals rather than generating their own. Because of using backscatter communication, Ambient IoT devices are also known as backscatter devices (BD) in the literature.  Ambient IoT can support many different use cases. Nevertheless, in general, the Ambient IoT use cases can be characterized by four different use case categories:

• Inventory taking: With inventory taking, the main purpose is to discover what goods (e.g. boxes, containers, packages, tools) are present in a specific area. Upon request sent by the network within the specific area, Ambient IoT devices attached to these goods report an identifier associated with the good, possibly supplemented with other information such as status, measurement results, and/or location.
• Sensor data collection: With sensor data collection, the Ambient IoT device is associated with a sensor. Transfer of sensor data can be initiated by the Ambient IoT device, e.g. periodically or when the Ambient IoT device has power or can be triggered by the network.
• Asset tracking: With asset tracking, the main purpose is to determine the location of goods. Ambient IoT devices attached to these goods report an identifier associated with the goods. This can then be combined with location information. Asset tracking can also be initiated by an Ambient IoT capable UE (i.e. a UE that can communicate with an Ambient IoT device), thus finding the location of Ambient IoT devices within a particular range of the UE.
• Actuator control: With actuator control, the Ambient IoT device is associated with an actuator. Transfer of actuator commands is generally initiated by the network.

Research Directions:

• Modulation and Waveform Design – Developing efficient backscatter modulation schemes in terms of power consumption and detection reliability under different channel conditions. Also, investigating the most suitable waveform candidate for AMP IoT.
• Direct Link Interference-self interference Suppression and Cancellation – Mitigating interference in ambient IoT systems for both mono-static and bi-static architecture.
• Channel Coding – Enhancing detection reliability through low-complexity and power-efficient coding techniques.
• Channel Modeling – modeling and characterizing wireless propagation in AMP IoT as compared to the conventional channel models.
• Synchronization and Timing – Addressing timing mismatches and synchronization issues in backscatter-based IoT networks.
• Multiple Access and Scheduling – Designing efficient multiple access protocols to enable multi-device connectivity with minimal energy consumption.
• Duplexing Schemes – Design feasibility of full-duplex and half-duplex mechanisms for ambient IoT.
• Millimeter-wave Symbiotic Radio- Designing ambient IoT systems operating on mmWave backscatter communication for Mbps of data rates to support VR and XR technologies.

The continuous evolution of wireless communication aims to enhance both information rate and reliability, driven by the increasing demand for connectivity. Among the key technological advancements, multiple-antenna systems—culminating in Massive MIMO—have played a pivotal role, with their latest realization in 5G. By further increasing the number of antenna elements, spatial orthogonality improves, leading to higher spatial multiplexing gain, diversity gain, and enhanced spectral efficiency.

In the downlink, employing a large number of antennas at the base station allows precise control over the radiated electromagnetic field, enabling beamforming to direct signals toward specific users while mitigating interference. When MIMO arrays become extremely dense, forming extra-large antenna arrays (ELAA) and converging toward holographic communications, the system can fully exploit the electromagnetic propagation characteristics, approaching fundamental wireless capacity limits. Beyond 5G and 6G networks are expected to integrate these extremely large MIMO arrays (XL-MIMO) or Ultra-massive MIMO Systems to support ultra-dense communication scenarios such as stadiums, malls, smart cities, and industrial IoT networks.

With XL-MIMO, new propagation characteristics emerge that differ significantly from conventional MIMO systems. The Rayleigh distance extends to tens or hundreds of meters, bringing many users into the near-field region, where electromagnetic waves exhibit spherical wavefronts rather than planar approximations. Traditional far-field models, based on plane-wave approximations, lead to magnitude and phase errors due to the non-negligible curvature of spherical waves. Additionally, the spherical wavefronts allow beam focusing on both angle and distance compared to the conventional MIMO beamforming. These aspects necessitate new signal processing techniques tailored to near-field communication, diverging from conventional far-field MIMO strategies.

Advantages of Near-Field XL-MIMO Over Conventional Far-Field MIMO

1. High-Rank LoS MIMO Channels: The wave curvature increases the rank of the Line-of-Sight (LoS) MIMO channel matrix, enabling independent data streams to be transmitted efficiently in high SNR regimes.
2. Beam Focusing: Spherical wavefronts allow beam focusing in both angle and distance, supporting localization division multiple access (LDMA) and improving user multiplexing in dense networks.
3. Enhanced SNR: Near-field beam focusing provides higher SNR compared to far-field beamforming, improving link data rate.
4. Improved Spatial Resolution: The increased spatial resolution facilitates precise user localization, enhancing positioning capabilities in communication networks.

Challenges and Limitations of Near-Field XL-MIMO

1. High Complexity in Channel Estimation: While large antenna arrays offer greater beamforming flexibility, they introduce prohibitive complexity in channel estimation and acquisition.
2. 2D Beam Training and Tracking Overhead: Beamforming in the near field depends on both angle and distance, requiring two-dimensional beam training, tracking, and scheduling, which increases system complexity.
3. Partial Visibility of Antenna Elements: Users may not be visible to all antenna elements, leading to sparse channels, rendering conventional channel estimation and equalization techniques inadequate.
4. Increased Channel Sparsity: The sparsity may extend across both angular and distance domains, further complicating channel estimation, acquisition and equalization.

Research Directions in Near-Field Communication

1. Near-Field Transceiver Design
   • Designing transceivers optimized for near-field communication requires new architectures that exploit spherical wavefront properties and support beam focusing.
   • It involves antenna array design, precoding strategies, and hardware constraints in ultra-massive MIMO.
2. User Multiplexing in Near-Field Communication
   • Conventional spatial multiplexing assumes far-field plane waves; in contrast, near-field multiplexing benefits from beam focusing on both angle and distance.
   • Therefore, investigating Localization Division Multiple Access (LDMA) and novel precoding techniques could improve spectral efficiency in ultra-dense environments.
3. Spherical Wavefront and Spatial Multiplexing in LoS Ultra-Massive MIMO
   • Line-of-Sight (LoS) MIMO typically suffers from low-rank channel matrices, but near-field propagation increases rank due to wave curvature.
   • Exploring LoS-MIMO spatial multiplexing in ultra-massive MIMO systems can unlock new capacity scaling laws.
4. Near-Field Channel Estimation and Characterization
   • Near-field propagation differs significantly from conventional plane-wave models, requiring accurate channel models and estimation techniques that consider distance-dependent wavefronts.
   • Research could explore data-driven approaches (machine learning), compressed sensing, and parametric modeling to improve estimation accuracy and reduce complexity.
5. Near-Field Channel Sparsity and Compressed Sensing-Based Algorithms

• • Near-field channels exhibit spatial and angular sparsity, making compressed sensing techniques effective for channel estimation and feedback reduction.
  • This research could explore dictionary learning, deep-learning-based sparse recovery, and low-complexity sparse estimators.

6. Near-Field and Far-Field User Coexistence/Hybrid Field Multi-User XL-MIMO Systems

• • • Next-generation networks must support hybrid field scenarios, where some users are in the near field while others remain in the far field, or one user may receive different data streams from the two fields simultaneously.
    • Investigating hybrid beamforming, dual-mode precoding, and adaptive antenna configurations is critical for efficient multi-user performance.

• • Investigating resources and power allocation in Hybrid Field Multi-User XL-MIMO Systems is also an open problem. This topic involves beamforming optimization, power control, and interference mitigation

7. Waveform Design for Narrowband and Wideband Near-Field Communication

• • Near-field effects differ in narrowband vs. wideband systems due to frequency-dependent wavefront curvature.
  • This research could focus on orthogonal waveform design, modulation schemes, and digital baseband interference management for near-field communication.

8. Representation and Characterization of the Effective Channel in Narrowband and Wideband Near-Field Communication (6G Candidate Waveforms)

• • Effective channel modeling is crucial for designing 6G-compatible transmission strategies.
  • This topic involves deriving analytical models and empirical validation for near-field channels across different bandwidth regimes.

9. Low-Overhead Beam Training, Tracking, and Scheduling in Near-Field Ultra-Massive MIMO

• • Beamforming in the near field requires tracking both angle and distance, significantly increasing training overhead.
  • Developing efficient beam training, adaptive tracking, and scheduling techniques is necessary for reducing latency and complexity.

  

Integrated Sensing and Communication (ISAC) is a transformative technology for 5G and beyond, addressing the growing spectrum scarcity and enabling next-generation applications that require joint communication and sensing capabilities. The rapid evolution of wireless networks has given rise to advanced use cases such as autonomous driving, air traffic control, geophysical monitoring, smart surveillance, and security systems, all of which demand high-precision sensing alongside data transmission.

As modern wireless applications increasingly operate in spectrum bands traditionally reserved for radar systems, classical communication architectures must be redefined to incorporate radar sensing functionalities. Unlike conventional communication, where cooperative transceivers exchange data, radar actively transmits known signals toward a target and processes the received echoes to extract critical information such as range, velocity, Doppler shift, and angular parameters. ISAC aims to unify these functionalities, leveraging shared resources for enhanced spectral efficiency, lower hardware costs, and improved system adaptability. However, several key challenges must be addressed to achieve optimal ISAC performance.

Key Challenges and Research Directions:

1. 1. Waveform and Frame Design for ISAC – Optimizing signal structures to balance sensing and communication performance.
   2. Security and Privacy in ISAC – Developing secure waveforms, privacy-preserving protocols, and physical layer security techniques.
   3. Interference Management – Mitigating mutual interference between radar and communication systems sharing the same spectrum.
   4. Advanced Signal Processing – Developing novel algorithms for joint parameter estimation, detection, and data transmission.
   5. THz Sensing and Communication – Leveraging high-frequency bands for improved resolution and data rates.
   6. Machine Learning for ISAC – Enhancing adaptability and efficiency in dynamic environments.
   7. Channel Modeling for ISAC – Understanding propagation effects unique to dual-functionality systems.
   8. Antenna and Beamforming Strategies – Designing joint beamforming and IRS-assisted solutions to optimize sensing and communication.
   9. Near-Field ISAC in XL-MIMO – Addressing spherical wavefront propagation and polarization-aware beamforming for high-precision ISAC.
   10. ISAC in Aerial and Space Networks – Developing UAV-assisted and satellite-based ISAC frameworks for global coverage.

Millimeter-wave (mmWave) and terahertz (THz) bands are key enablers of ultra-high-speed wireless communication and high-resolution sensing in 6G and beyond wireless networks. While mmWave (30–100 GHz) is already deployed in 5G, THz (0.1–10 THz) offers massive bandwidth for Tbps data rates and sub-millimeter level precision sensing. However, these bands suffer from high pathloss, limited coverage, severe Doppler effects, and non-stationary channel characteristics, demanding advanced solutions. Key research directions include ultra-massive and extra-large multiple-input multiple-output (UM-MIMO, XL-MIMO) systems with intelligent beamforming, joint sensing and communication (JSAC) frameworks, AI-driven beam tracking, and adaptive waveform design. Addressing molecular absorption losses, temporal broadening effect, extended near-field, and beam squint is crucial for robust deployment. Furthermore, integrating mmWave and THz with non-terrestrial networks (NTNs), including satellites, unmanned aerial vehicles (UAVs), and high-altitude platforms (HAPs), will expand their applications in global connectivity, environmental monitoring, and security. Overcoming hardware complexity, and advanced transceiver designs including energy efficiency will define the next generation of wireless innovation.

6G application scenarios, (a) simultaneous spatial situational awareness and ground communication, (b) smart vehicular systems, (c) surface(s)-assisted indoor ISAC system, (d) miscellaneous high-frequency applications.

Key Research Directions

• Ultra-massive and extra-large MIMO (UM-MIMO, XL-MIMO) systems: High-gain arrays with adaptive beamforming to counter path loss and non-stationary effects.
• JSAC: Leveraging THz reflections, surface scattering, and micro-Doppler for simultaneous data transmission and high-resolution sensing.
• AI-Driven Optimization: Predictive beam tracking, power control, and resource allocation to enhance reliability.
• Adaptive Waveform Design: Doppler-resilient waveforms, (orthogonal time frequency space) OTFS waveform for doubly selective channels, and pulse shaping for ISI mitigation.
• Reconfigurable Intelligent Surfaces (RIS): Passive reflecting elements to enhance coverage and non-line-of-sight connectivity.
• Near-Field JSAC: Exploiting spherical wavefronts for precise localization and enhanced multiplexing.
• Integration with NTNs: THz-enabled CubeSats, UAVs, and HAPs for spaceborne sensing and high-speed wireless backhaul.

With the advent of 6G networks, distributed MIMO and cell-free massive MIMO (CF-mMIMO) are emerging as promising technologies to overcome the limitations of traditional cellular systems. Unlike conventional networks, where user equipment (UE) is served by a single base station, CF-mMIMO provides seamless coverage by enabling multiple access points (APs) to cooperatively serve users. This results in enhanced spectral efficiency, reduced interference, and uniform connectivity, making it particularly suitable for ultra-reliable low-latency communication (URLLC) and integrated sensing and communication (ISAC) applications.

Joint Sensing and Communication in CF-mMIMO: Traditional CF-mMIMO networks primarily focus on communication, neglecting the role of radar-based sensing in user association (UA). Our research introduces a novel dynamic user association mechanism, where sensing information (e.g., radar echoes) assists in mitigating pilot contamination and optimizing AP selection. This approach refines UA by considering signal-to-clutter-plus-noise ratio (SCNR), ensuring that APs with clutter-free paths provide the most reliable service.

Pilot Contamination Mitigation via Sensing for Distributed MIMO Networks: One of the main challenges in distributed MIMO systems is pilot contamination, where overlapping pilot sequences lead to degraded channel state information (CSI) estimation. By integrating sensing-assisted contamination identification, we leverage radar-based AoA (Angle of Arrival) and range estimation to detect pilot contamination in real-time. This allows for dynamic re-association of users, ensuring robust connectivity.

Multi-Connectivity for ISAC Networks: Multi-connectivity is crucial for mobility and reliability in next-generation networks. Our research explores optimal UE clustering mechanisms, where each user is connected to multiple APs, dynamically adapting to channel conditions. We integrate beamforming techniques and coherent joint processing across APs, improving throughput and reducing latency in real-time applications such as autonomous vehicles and industrial IoT.

Current Efforts

1. Enhancing radar-aided UA in cell-free mMIMO.
2. Intelligent pilot assignment strategies to reduce interference in CF-mMIMO.
3. Dynamic clustering for mobility management and load balancing.
4. SCNR-based AP selection for joint communication and sensing.
5. Exploring cooperative AP coordination strategies for cell-free MIMO.
6. Energy-efficient distributed processing techniques for large-scale CF-mMIMO networks.
7. Developing joint sensing and communication-aware scheduling algorithms for CF-mMIMO.

Interference: A Barrier to Achieving a Fully Connected World

Interference remains one of the most significant challenges in wireless communication, hindering progress toward a truly interconnected world. New wireless standards aim to connect everyone and everything at any time, enabling a wide range of services and applications that demand increasing data rates, higher reliability, and higher security. Achieving this, however, requires overcoming interference while maintaining an affordable system capacity, manageable complexity, and low latency.

Understanding the Sources of Interference

Interference in wireless networks can be broadly categorized into two main types based on the interest of user: Self-User Interference (SUI) and Other-User Interference (OUI). To further refine this classification, we can distinguish between intentional and unintentional interference, which results in four distinct categories: Intentional Self-User Interference (ISUI), Unintentional Self-User Interference (U-SUI), Intentional Other-User Interference (I-OUI), and Unintentional Other-User Interference (U-OUI).

The Influence of Network Factors on Interference

Several factors shape nature and intensity of interference in a wireless network. These include:

• The wireless channel itself, which is influenced by environmental conditions and signal propagation.
• Hardware limitations, which can exacerbate interference when devices work non-linearly.
• The waveform used for communication, which affects the efficiency and interference resistance of the transmission.
• Resource reuse, which involves the sharing of frequencies or time slots between multiple users, often leading to interference.
• Asynchronous transmission, where devices transmit data at slightly different times, further complicating interference management.

Managing Interference: A Multidimensional Approach

Effective interference management requires a comprehensive approach that spans multiple domains of wireless communication:

1. Wireless Channel Management: Techniques like power control, beamforming, channel shortening, equalization, and path diversity are employed to minimize interference at the channel level.
2. Waveform Coexistence: Managing the coexistence of different waveforms ensures efficient use of available spectrum while minimizing interference.
3. Radio Access Techniques: Methods such as semi-persistent scheduling and grant-free access help optimize the use of network resources, reducing interference between users.
4. Frame Design: Techniques like filtering, numerology design, and windowing allow for better control of how data is transmitted, reducing interference within the frame structure.

Key Research Areas in Interference Management

• Orthogonal and Non-Orthogonal Multiple Access: Examining how different access schemes can help manage interference in both dense and sparse network environments.
• Coordinated Networks: Investigating how cooperation between network elements can reduce interference and enhance network performance.
• Machine Learning for Interference Management: Applying ML techniques to:
  • Analysis the stochastic nature of interference to predict and mitigate its effects.
  • Aid in channel and waveform interference management through adaptive algorithms.
  • Mitigate RF impairments, improving overall network performance.
• Interference Exploitation for Sensing: Exploring how interference can be strategically used for sensing applications, such as radar and environmental monitoring.
• Spectrum Sharing: Investigating spectrum sharing mechanisms between terrestrial and non-terrestrial networks to improve efficiency and reduce interference.
• Coexistence of Different Wireless Standards: Studying how various wireless standards can coexist within the same spectrum, minimizing interference between them.

The increasing demand for high-bandwidth applications, combined with the rapid development of wireless devices, has contributed to the advancement of wireless communication systems. However, because spectrum is a limited resource, increasing spectral congestion needs more effective spectrum utilization solutions. This brought the idea of cognitive radio (CR) which has spectrum sensing, awareness of its surroundings, learning and self-adapting capabilities to maintain communication in an opportunistic manner. To enhance CR efficiency in next-generation networks, AI-driven cognitive radio networks (CRNs) utilize deep learning in spectrum sensing and dynamic decision-making. Reconfigurable Intelligent Surfaces (RIS) enhance the radio environment, and Full-Duplex CR (FD-CR) enables simultaneous reception and transmission to deliver better spectral efficiency. Additionally, Integrated Sensing and Communication (ISAC) enables real-time spectrum management to serve low-latency, high-reliability applications like autonomous vehicles, smart cities, and industrial IoT. All of this makes CR a key enabler of efficient and adaptive wireless communication. At Cosinc Group, our research focuses on advancing CR and Software-Defined Radio (SDR) algorithms to empower future wireless systems with enhanced adaptability, intelligence, and efficiency. Additionally, we are committed to designing and implementing CR testbeds in our state-of-the-art laboratory, where we evaluate the performance of our proposed techniques in real-world scenarios to bridge the gap between theory and practice.

Current Efforts

• Dynamic spectrum access
• Spectrum sensing and spectrum shaping
• Signal intelligence (SigINT) and identification
• Channel awareness and real-time channel parameters estimation
• User awareness and user context estimation
• Mobility modeling and estimation
• Adaptive waveform and signal design
• Cognitive radio measurements and metrics design

The expectations for the applications that 6G will carry are tremendous, ranging from integrated sensing and communication to V2X, UAV, NTN, IoT, etc., which makes 6G networks expand vertically to other fields, in contrast to what have been from 1G till 5G; 6G is going to be for everything. This has placed a significant strain on the physical layer signal interface, potentially challenging the limitations of the legacy 5G waveform

Our primary research focus lies in developing novel waveform designs to meet the demands of heterogeneous networks. To this end, our work spans from exploring the fundamental characteristics of waveforms—such as pulse shaping, orthogonality and nonorthogonality, time-frequency lattice structures, and delay-Doppler-angular resolution—to analyzing their integration within transmission frame structures.

Beyond studying waveforms in the time-frequency domain with respect to channel adaptability and flexibility, we also examine inter-waveform relationships, including signal separability and their suitability for next-generation networks. Additionally, we explore multidimensional modulation techniques, such as OTFS (2D), for advanced waveform design.

Considering practical implementations, we further investigate their impact on key physical layer aspects (e.g., PAPR, equalization, channel estimation, and out-of-band radiation) and MAC layer functions (e.g., scheduling), as these factors play a crucial role in shaping transmitter and receiver algorithms.

Our Research Focus at CoSiNC Lab:

• Hybrid waveform design for beyond 5G,
• Ultra-flexible and scalable OFDM-based waveform design,
• Waveform design for the robustness to doubly dispersive channels,
• Waveform design for integrated sensing and communication,
• Waveform design for multi-point to multi-point networks.
• Waveforms coexistence and spectrum sharing using waveform domain NOMA.
• Waveform design for low power applications: IoT, Ambient IoT, and wake-up signals.

3GPP Rel-18 introduces a low-power wake-up mechanism to address this. Instead of continuous polling, UEs can enter deep sleep and be awakened by a dedicated “Wake-Up Signal” (WUS) from the network. This relies on a separate, ultra-low power Wake-Up Receiver (WUR), allowing devices to save energy.

The design of WUS is critical for minimizing power use and ensuring reliable detection by the WUR, with applications in IoT, wearables, XR glasses, and smart homes. Rel-18 aims to revolutionize energy efficiency, delivering major power savings while maintaining compatibility with existing UEs. This marks a shift towards more sustainable 5G connectivity, where devices only wake when needed.

The LP-WuS is designed as an ON-OFF keying signal in the time domain, generated by preprocessing OFDM frequency components while coexisting with legacy NR signals within the same OFDM symbol. It is transmitted periodically in predefined time slots, enabling the LP-WuR to operate in either continuous or periodic monitoring modes. The LP-WuR is expected to accommodate a range of device capabilities, from basic time-domain correlation to a fully functional OFDM-based receiver.

Our Research Focus at CoSiNC Lab:

• Low power wake-up and synchronization signals design.
• Low power wake-up radio design with minimum implementation complexity.
• Interference management between legacy NR signals and wake-up signals.
• Handover and radio resource measurements using wake-up signals.
• Exploit LP-WuS for channel estimation, sensing and users grouping.

The sixth generation (6G) wireless networks represent a paradigm shift from data rate-centric designs toward multifunctional systems that integrate communication and environmental sensing capabilities. This enhanced functionality, while beneficial for network performance, introduces vulnerabilities to security threats including eavesdropping, spoofing, impersonation and jamming attacks. Although commonly utilized, conventional cryptographic approaches exhibit limitations in modern heterogeneous wireless environments due to their requirements for uniform computational capabilities across all nodes. Furthermore, physical layer security (PLS) offers a complementary security framework that leverages intrinsic wireless channel characteristics to ensure communication confidentiality, integrity, along with node/message authentication and malicious nodes’ detection by exploiting the dynamic characteristics of wireless environments.

Artificial Intelligence, on the other hand, is revolutionizing PLS through its capability for adaptive, real-time threat detection using advanced pattern recognition and automatically adjust defense mechanisms, establishing a dynamic security framework essential for protecting the increasingly complex and heterogeneous communication environments of next-generation wireless networks.

Current Efforts:

1. Physical layer security for 5G/6G networks.
2. Adaptive Defense Systems: Cognitive Security Frameworks,
3. Cross-layer and hybrid security approaches.
4. Radio Environment Mapping (REM) security.
5. Physical layer authentication (MIMO, XL-MIMO near-field, RIS).
6. Context-aware security frameworks.
7. Advanced Artificial Intelligence (AI) security mechanisms.
8. Reconfigurable Intelligent Surfaces (RIS) assisted security.
9. Secure waveform design techniques.
10. Resilient resource allocation algorithms for network protection.
11. Safeguarding Space-Ground Connectivity: Security for Non-Terrestrial Networks.
12. Trustworthy integrated sensing and communications (ISAC) frameworks.

Multiple access, scheduling, and resource management form the backbone of modern wireless systems, orchestrating how users share limited spectrum resources across time, frequency, and spatial domains. In current 4G and 5G networks, Orthogonal Frequency Division Multiplexing (OFDM) underpins most transmission strategies due to its spectral efficiency and robustness against multipath fading. OFDM supports a variety of multiple access (MA) schemes, including traditional orthogonal methods such as Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), and Orthogonal Frequency Division Multiple Access (OFDMA), as illustrated in the figures. These schemes exploit different degrees of freedom—primarily time and frequency—to multiplex users and enhance spectral efficiency.

As wireless systems evolve toward 6G, OFDM-based access mechanisms begin to show limitations in satisfying the ultra-low latency, high reliability, and high-mobility requirements of next-generation applications. This has led to growing interest in advanced waveform designs such as Orthogonal Time Frequency Space (OTFS), Orthogonal Time Sequency Multiplexing (OTSM), and Affine Frequency Division Multiplexing (AFDM). These waveforms are inherently more robust to doubly dispersive channels and introduce new resource domains such as the delay-Doppler plane. In such settings, resource allocation and user scheduling strategies must be redefined to align with the underlying waveform structure, while also supporting service-aware differentiation across enhanced Mobile Broadband (eMBB), ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC).

Beyond OFDM, these new waveforms enable additional MA strategies—such as delay division, Doppler division, sequency division, and hybrid approaches—that are tailored to the unique characteristics of each domain. As shown in the figures, many combinations are possible depending on the waveform’s operational domain and design considerations. They can also be affected by guard intervals and pilot placements. Selecting the appropriate MA scheme depends on factors such as the service requirements and the channel conditions experienced by the users.

Our Research Focus at CoSiNC Lab:

At CoSiNC Lab, our research in this domain targets both the theoretical foundations and practical implementation of next-generation multiple access and resource management strategies. We focus on designing practical multiple access schemes that enable efficient and scalable network deployment. Additionally, we are exploring how heterogeneous waveform types can coexist and operate harmoniously through the use of adaptive multiple access and scheduling techniques.

The efforts to optimize wireless link performance have traditionally focused on improving the transmitter (encoding, power amplification, antenna design etc.) as well as the receiver. The RF channel itself, however, has long been viewed as a fixed element, unchangeable by the user once operational choices are made. While some frequencies, paths, and conditions yield better results, users have had little ability to alter them post-operation. Though some advanced systems can adjust transmitter and receiver settings to adapt to channel changes like attenuation, noise, or distortion with a compromise in power consumption, yet the channel's core characteristics remain static.

A new technology envisioned for B5G or 6G, the reconfigurable intelligent surface (RIS), introduces a way to modify the channel environment, enhances performance by improving signal-to-noise ratio (SNR), reducing bit error rate (BER) etc. It is a 2D reflective surface made up of numerous array elements (mostly passive) that can be dynamically reconfigured to alter the RF path's characteristics like time and phase-shifting, as well as changing the angle of reflection, that compensate for the uncontrollable propagation of the path and therefore improve the channel characteristics for improved system performance.

Research Priorities at CoSiNC Lab:

At CoSiNC Lab, our research is dedicated to investigating various facets of RIS and their practical implementation. Our goal is to overcome essential issues associated with it and promote practical uses of RIS by concentrating on these key domains:

• RIS-assisted networks for efficient handover processes.
• RIS-aided near-field communication for 6G
• Hybrid RIS assisted communication for disaster scenarios.
• Positioning and sensing with RIS.
• Beyond these, we delve into network-controlled repeaters to extend coverage, as well as back-scatter communication to enable low-power, passive data transmission for energy-efficient networks.