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1. Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map

   https://doi.org/10.1109/TWC.2024.3360872  

   Zhang, Songyang ; Yu, Tianhang ; Choi, Brian ; Ouyang, Feng ; Ding, Zhi ( January 2024 , IEEE Transactions on Wireless Communications)

   Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless systems. Generally, radiomaps are constructed from radio strength measurements by deployed sensors and user devices. However, not all areas are accessible for radio measurements due to physical constraints and security considerations, leading to non-uniformly spaced measurements and blanks on a radiomap. In this work, we explore distribution of radio spectrum strengths in view of surrounding environments, and propose two radiomap inpainting approaches for the reconstruction of radiomaps that cover missing areas. Specifically, we first define a propagation based priority before integrating exemplar-based inpainting with radio propagation model for fine-resolution small-size missing area reconstruction on a radiomap. We next introduce a novel radio depth map and propose a two-step template-perturbation approach for large-size restricted region inpainting. Our experimental results demonstrate the power of the proposed propagation priority and radio depth map in capturing PSD distribution, as well as their efficacy in radiomap reconstruction. 

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2. ProgRPGAN: Progressive GAN for Route Planning

   https://doi.org/10.1145/3447548.3467406  

   Fu, Tao-yang ; Lee, Wang-Chien ( August 2021 , Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021))

   Learning to route has received significant research momentum as a new approach for the route planning problem in intelligent transportation systems. By exploring global knowledge of geographical areas and topological structures of road networks to facilitate route planning, in this work, we propose a novel Generative Adversarial Network (GAN) framework, namely Progressive Route Planning GAN (ProgRPGAN), for route planning in road networks. The novelty of ProgRPGAN lies in the following aspects: 1) we propose to plan a route with levels of increasing map resolution, starting on a low-resolution grid map, gradually refining it on higher-resolution grid maps, and eventually on the road network in order to progressively generate various realistic paths; 2) we propose to transfer parameters of the previous-level generator and discriminator to the subsequent generator and discriminator for parameter initialization in order to improve the efficiency and stability in model learning; and 3) we propose to pre-train embeddings of grid cells in grid maps and intersections in the road network by capturing the network topology and external factors to facilitate effective model learning. Empirical result shows that ProgRPGAN soundly outperforms the state-of-the-art learning to route methods, especially for long routes, by 9.46% to 13.02% in F1-measure on multiple large-scale real-world datasets. ProgRPGAN, moreover, effectively generates various realistic routes for the same query. 

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3. Differentially-Private Incentive Mechanism for Crowdsourced Radio Environment Map Construction

   https://doi.org/10.1109/INFOCOM.2019.8737512  

   Hu, Yidan ; Zhang, Rui ( April 2019 , IEEE INFOCOM 2019 - IEEE Conference on Computer Communications)

   Database-driven Dynamic Spectrum Sharing (DSS) is a promising technical paradigm for enhancing spectrum efficiency by allowing secondary user to opportunistically access licenced spectrum channels without interfering with primary users' transmissions. In database-driven DSS, a geo-location database administrator (DBA) maintains the spectrum availability in its service region in the form of a radio environment map (REM) and grant or deny secondary users' spectrum access requests based on primary users' activities. Crowdsourcing-based spectrum sensing has great potential in improving the accuracy of the REM at the DBA but requires strong incentives and privacy protection to simulate mobile users' participation. To tackle this challenge, this paper introduces a novel differentially-private reverse auction mechanism for crowdsourcing-based spectrum sensing. The proposed mechanism allows the DBA to select spectrum sensing participants under a budget constraint while offering differential bid privacy, approximate truthfulness, and approximate accuracy maximization. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of the proposed mechanism. 

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4. Differentially-Private Incentive Mechanism for Crowdsourced Radio Environment Map Construction

   Hu, Yidan ; Zhang, Rui ( April 2019 , IEEE International Conference on Computer Communications)

   Database-driven Dynamic Spectrum Sharing (DSS) is a promising technical paradigm for enhancing spectrum efficiency by allowing secondary user to opportunistically access licenced spectrum channels without interfering with primary users' transmissions. In database-driven DSS, a geo-location database administrator (DBA) maintains the spectrum availability in its service region in the form of a radio environment map (REM) and grant or deny secondary users' spectrum access requests based on primary users' activities. Crowdsourcing-based spectrum sensing has great potential in improving the accuracy of the REM at the DBA but requires strong incentives and privacy protection to simulate mobile users' participation. To tackle this challenge, this paper introduces a novel differentially-private reverse auction mechanism for crowdsourcing-based spectrum sensing. The proposed mechanism allows the DBA to select spectrum sensing participants under a budget constraint while offering differential bid privacy, approximate truthfulness, and approximate accuracy maximization. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of the proposed mechanism. 

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5. Deep Spectrum Cartography Using Quantized Measurements

   https://doi.org/10.1109/ICASSP49357.2023.10096058  

   Timilsina, Subash ; Shrestha, Sagar ; Fu, Xiao ( June 2023 , IEEE)

   Spectrum cartography (SC) techniques craft multi-domain (e.g., space and frequency) radio maps from limited measurements, which is an ill-posed inverse problem. Recent works used low-dimensional priors such as a low tensor rank structure and a deep generative model to assist radio map estimation---with provable guarantees. However, a premise of these approaches is that the sensors are able to send real-valued feedback to a fusion center for SC---yet practical communication systems often use (heavy) quantization for signaling. This work puts forth a limited feedback-based SC framework. Similar to a prior work, a generative adversarial network (GAN)-based deep prior is used in our framework for fending against heavy shadowing. However, instead of using real-valued feedback, a random quantization strategy is adopted and a maximum likelihood estimation (MLE) criterion is proposed. Analysis shows that the MLE provably recovers the radio map, under reasonable conditions. Simulations are conducted to showcase the effectiveness of the proposed approach. 

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