Cellular-Base-Station Assisted Device-to-Device Communications in TV White Space (1506.01394v1)

Abstract: This paper presents a systematic approach to exploit TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a look-up table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is firstly introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking, D2D communications in hotspots such as subway, game stadiums, and airports, etc.

• The paper investigates using TV white space for cellular-assisted device-to-device communications by developing a location-specific database for managing interference, employing matrix completion, boundary detection, and mobile crowd sensing techniques.
• Numerical results demonstrate that the proposed approach effectively enables D2D communications in TVWS while respecting interference constraints, showing substantial improvements in spatial reuse and interference reduction compared to traditional methods.
• The research highlights practical implications for optimizing spectrum utilization and potential applications in challenging environments, also setting groundwork for future studies on coexistence strategies and the role of AI in spectrum management.

Cellular-Base-Station Assisted Device-to-Device Communications in TV White Space

The paper provides a thorough investigation into using TV white space (TVWS) for device-to-device (D2D) communications with support from existing cellular infrastructures. The primary aim of the paper is the development of a location-specific TVWS database intended to manage D2D link emissions within unlicensed digital TV (DTV) bands, while satisfying the interference constraints imposed by licensed DTV services.

Main Contributions

• Matrix Completion: To address the challenge of incomplete spectrum measurements from personal mobile devices, the authors employ a matrix completion strategy. Specifically, they apply the fixed point continuation algorithm to fill unknown measurements, ensuring a complete dataset for further analysis.
• Boundary Detection: The paper leverages nonlinear support vector machines to identify the irregular coverage boundaries of a licensed DTV transmitter. This knowledge aids in opportunistic spatial reuse—optimizing the emission power of D2D links without interfering with existing DTV services.
• Mobile Crowd Sensing: By harnessing spectrum measurements from personal mobile devices, the authors introduce a novel approach that utilizes mobile crowd sensing. This method contributes to a large pool of data that informs the TVWS database, thus enabling efficient spectrum management.

Numerical Results

Simulation outcomes show that the outlined approach facilitates D2D communications within TVWS, strictly adhering to interference constraints. Notably, the simulations demonstrate substantial benefits over traditional model-based methods—highlighting improved spatial reuse opportunities and reduced interference to DTV services.

Implications and Future Directions

From a practical perspective, the development of a mobile crowd sensing-enabled TVWS database represents a significant step towards optimizing spectrum utilization. Furthermore, the prospect of exploring TVWS within DTV protection regions opens up new avenues for enhancing wireless communications in complex environments such as underground parking and dense urban areas.

Theoretically, the paper invites further exploration into coexistence strategies for multiple D2D links sharing the same TV channel, addressing mutual interference conundrums. This research sets a groundwork for advancing spectrum sharing techniques, crucial for the evolution of next-gen wireless systems. As AI and data analytics continue to evolve, they may offer novel strategies for enhancing the accuracy and efficiency of spectrum usage assessments, potentially leading to more robust frameworks for cognitive radio networks.

In conclusion, this paper contributes meaningfully to the understanding and exploitation of TVWS for D2D communications, underscoring significant advantages and future potential in the domain of spectrum management.