Citation: | Li Xiangyang, Wang Shanyue, Zhang Chi, Yan Yubo, Tan Haisheng. Enhanced Capacity for Multi-Tag Concurrent Backscatter Communication Systems under Spectrum Constraints[J]. Journal of Computer Research and Development, 2024, 61(11): 2776-2792. DOI: 10.7544/issn1000-1239.202440401 |
Passive backscatter communication technology, due to its microwatt-level power consumption, makes IoT devices easy to be deployed and maintenance-free. The multi-tag concurrent communication technology enables tags to avoid complex protocols such as collision avoidance, thus reducing device power consumption while increasing the system throughput and scale. However, the limited spectrum resources available to backscatter tags lead to issues of mutual interference and low spectrum utilization in multi-tag concurrency. To this end, we propose CamScatter, a capacity-enhanced multi-tag parallel backscatter communication system. We design strategies for maximizing channel division and efficient allocation with limited spectrum resources, avoiding interference among tags, and significantly enhancing the system capacity. In the preprocessing phase, a bandwidth-maximized channel division algorithm is proposed, providing multiple interference-free channel division schemes for system communication. In the initialization phase, an optimized channel and rate allocation scheme is proposed, assigning the most suitable channels and rates to tags based on the signal-to-noise ratio (SNR) of all tags in the system, thereby mitigating signal interference caused by tag energy differences and improving system throughput. Additionally, during system working, it uses sideband aggregation and matched filtering techniques to enhance signals strength of the tags to be demodulated, reducing interference from other tags. The bandwidth range that this system utilizes is from 142.4 kHz to 773.5 kHz. Due to harmonic interference limitations, the theoretical maximum spectrum utilization rate is 81.1%, and the maximum system capacity without interference is
[1] |
Muratkar T S, Bhurane A, Kothari A. Battery-less Internet of things –A survey[J]. Computer Networks, 2020, 180: 107385 doi: 10.1016/j.comnet.2020.107385
|
[2] |
Van Huynh N, Hoang D T, Lu Xiao, et al. Ambient backscatter communications: A contemporary survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(4): 2889−2922
|
[3] |
Rostami M, Sundaresan K, Chai E, et al. Redefining passive in backscattering with commodity devices[C]//Proc of the 26th Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2020: 1–13
|
[4] |
Li Songfan, Zhang Chong, Song Yihang, et al. Internet-of-microchips: Direct radio-to-bus communication with SPI backscatter[C]//Proc of the 26th Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2020: 1–14
|
[5] |
Chakraborty T, Shi H, Kapetanovic Z, et al. Whisper: IoT in the TV white space spectrum[J]. GetMobile: Mobile Computing and Communications, 2023, 26(4): 32−35
|
[6] |
Peng Yao, Shangguan Longfei, Hu Yue, et al. PLoRa: A passive long-range data network from ambient LoRa transmissions[C]//Proc of the 2018 Conf of the ACM Special Interest Group on Data Communication. New York: ACM, 2018: 147–160
|
[7] |
Wang Shanyue, Yan Yubo, Chen Yujie, et al. Spray: A spectrum-efficient and agile concurrent backscatter system[J]. ACM Transactions on Sensor Networks, 2024, 20(2): 1−21
|
[8] |
Bharadia D, Joshi K R, Kotaru M, et al. BackFi: High throughput WiFi backscatter[C]//Proc of the 2015 ACM Conf on Special Interest Group on Data Communication. New York: ACM, 2015: 283–296
|
[9] |
Katanbaf M, Saffari A, Smith J R. MultiScatter: Multistatic backscatter networking for battery-free sensors[C]//Proc of the 19th ACM Conf on Embedded Networked Sensor Systems. New York: ACM, 2021: 69–83
|
[10] |
Ding Yuxin, Wang Shanyue, Mao Yachen, et al. VideoBack: High quality video backscatter with ambient WiFi[C]//Proc of 2023 IEEE 29th Int Conf on Parallel and Distributed Systems (ICPADS). Piscataway, NJ: IEEE, 2023: 1991–1998
|
[11] |
Yang Lei, Chen Yekui, Li Xiangyang, et al. Tagoram: Real-time tracking of mobile RFID tags to high precision using COTS devices[C]//Proc of the 20th Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2014: 237–248
|
[12] |
Yang Zhijian, Choudhury R R. Personalizing head related transfer functions for earables[C]//Proc of the 2021 ACM SIGCOMM Conf. New York: ACM, 2021: 137–150
|
[13] |
Mao Yachen, Yang Panlong, Wang Shanyue, et al. STABack: Making dynamic backscattering stable for fast and accurate object tracking[C]//Proc of 2023 IEEE/ACM 31st Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2023: 1–10
|
[14] |
Hessar M, Najafi A, Gollakota S. NetScatter: Enabling large-scale backscatter networks[C]//Proc of the 16th USENIX Symp on Networked Systems Design and Implementation (NSDI’19). Berkeley, CA: USENIX Association, 2019: 271−284
|
[15] |
Zhao Renjie, Zhu Fengyuan, Feng Yuda, et al. OFDMA-enabled Wi-Fi backscatter[C]//Proc of the 25th Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2019: 1–15
|
[16] |
Jiang Jinyan, Xu Zhenqiang, Dang Fan, et al. Long-range ambient LoRa backscatter with parallel decoding[C]//Proc of the 27th Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2021: 684–696
|
[17] |
Talla V, Hessar M, Kellogg B, et al. LoRa backscatter: Enabling the vision of ubiquitous connectivity[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2017, 1(3): 1−24
|
[18] |
Kellogg B, Parks A, Gollakota S, et al. Wi-fi backscatter: Internet connectivity for RF-powered devices[C]//Proc of the 2014 ACM Conf on SIGCOMM. New York: ACM, 2014: 607–618
|
[19] |
Zhang Pengyu, Bharadia D, Joshi K, et al. HitchHike: Practical backscatter using commodity WiFi[C]//Proc of the 14th ACM Conf on Embedded Network Sensor Systems CD-ROM. New York: ACM, 2016: 259–271
|
[20] |
Zhang Maolin, Chen Si, Zhao Jia, et al. Commodity-level BLE backscatter[C]//Proc of the 19th Annual Int Conf on Mobile Systems, Applications, and Services. New York: ACM, 2021: 402–414
|
[21] |
Chi Zicheng, Liu Xin, Wang Wei, et al. Leveraging ambient LTE traffic for ubiquitous passive communication[C]//Proc of the Annual Conf of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication. New York: ACM, 2020: 172–185
|
[22] |
Mazaheri M H, Chen A, Abari O. mmTag: A millimeter wave backscatter network[C]//Proc of the 2021 ACM SIGCOMM Conf. New York: ACM, 2021: 463–474
|
[23] |
Liu Xin, Chi Zicheng, Wang Wei, et al. VMscatter: A versatile MIMO backscatter[C]//Proc of the 17th USENIX Symp on Networked Systems Design and Implementation (NSDI’20). Berkeley, CA: USENIX Association, 2020: 895−909
|
[24] |
Varshney A, Harms O, Pérez-Penichet C, et al. LoRea: A backscatter architecture that achieves a long communication range[C]//Proc of the 15th ACM Conf on Embedded Network Sensor Systems. New York: ACM, 2017: 1–14
|
[25] |
Katanbaf M, Jain V, Smith J R. Relacks: Reliable backscatter communication in indoor environments[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(2): 1−24
|
[26] |
Galisteo A, Varshney A, Giustiniano D. Two to tango: Hybrid light and backscatter networks for next billion devices[C]//Proc of the 18th Int Conf on Mobile Systems, Applications, and Services. New York: ACM, 2020: 80–93
|
[27] |
Zhang Junbo, Soltanaghai E, Balanuta A, et al. PLatter: On the feasibility of building-scale power line backscatter[J]. GetMobile: Mobile Computing and Communications, 2022, 26(4): 19−22
|
[28] |
Zhu Fengyuan, Feng Yuda, Li Qianrui, et al. DigiScatter: Efficiently prototyping large-scale OFDMA backscatter networks[C]//Proc of the 18th Int Conf on Mobile Systems, Applications, and Services. New York: ACM, 2020: 42–53
|
[29] |
Wang Shanyue, Yan Yubo, Han Feiyu, et al. MultiRider: Enabling multi-tag concurrent OFDM backscatter by taming in-band interference[C]//Proc of the 22nd Annual Int Conf on Mobile Systems, Applications and Services. New York: ACM, 2024: 292–303
|
[30] |
Chen Yujie, Ding Yuxin, Wang Shanyue, et al. WiB-MAC: Collision-avoidance multiple access for Wi-Fi backscatter networks[C]//Proc of 2024 IEEE/ACM 32nd Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2024: 1−10
|
[31] |
Yen C C, Gutierrez A E, Veeramani D, et al. Radar cross-section analysis of backscattering RFID tags[J]. IEEE Antennas and Wireless Propagation Letters, 2007, 6: 279−281 doi: 10.1109/LAWP.2007.898552
|
[32] |
Nikitin P V, Rao K V S, Martinez R D. Differential RCS of RFID tag[J]. Electronics Letters, 2007, 43(8): 431−432 doi: 10.1049/el:20070253
|
[33] |
Dehbashi F, Abedi A, Brecht T, et al. Verification: Can wifi backscatter replace RFID?[C]//Proc of the 27th Annual Int Conf on Mobile Computing and Networking. New York: ACM, 2021: 97–107
|
[34] |
Abedi A, Dehbashi F, Mazaheri M H, et al. WiTAG: Seamless WiFi backscatter communication[C]//Proc of the Annual Conf of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication. New York: ACM, 2020: 240–252
|
[35] |
Proakis J G, Salehi M. Digital Communications[M]. 5th ed. Boston: McGraw-Hill, 2008
|
[1] | Fang Haotian, Li Chunhua, Wang Qing, Zhou Ke. A Method of Microservice Performance Anomaly Detection Based on Deep Learning[J]. Journal of Computer Research and Development, 2024, 61(3): 600-613. DOI: 10.7544/issn1000-1239.202330543 |
[2] | Yue Wenjing, Qu Wenwen, Lin Kuan, Wang Xiaoling. Survey of Cardinality Estimation Techniques Based on Machine Learning[J]. Journal of Computer Research and Development, 2024, 61(2): 413-427. DOI: 10.7544/issn1000-1239.202220649 |
[3] | Wang Rui, Qi Jianpeng, Chen Liang, Yang Long. Survey of Collaborative Inference for Edge Intelligence[J]. Journal of Computer Research and Development, 2023, 60(2): 398-414. DOI: 10.7544/issn1000-1239.202110867 |
[4] | Liu Qixu, Chen Yanhui, Ni Jieshuo, Luo Cheng, Liu Caiyun, Cao Yaqin, Tan Ru, Feng Yun, Zhang Yue. Survey on Machine Learning-Based Anomaly Detection for Industrial Internet[J]. Journal of Computer Research and Development, 2022, 59(5): 994-1014. DOI: 10.7544/issn1000-1239.20211147 |
[5] | Wang Jialai, Zhang Chao, Qi Xuyan, Rong Yi. A Survey of Intelligent Malware Detection on Windows Platform[J]. Journal of Computer Research and Development, 2021, 58(5): 977-994. DOI: 10.7544/issn1000-1239.2021.20200964 |
[6] | Chen Kerui, Meng Xiaofeng. Interpretation and Understanding in Machine Learning[J]. Journal of Computer Research and Development, 2020, 57(9): 1971-1986. DOI: 10.7544/issn1000-1239.2020.20190456 |
[7] | Liu Chenyi, Xu Mingwei, Geng Nan, Zhang Xiang. A Survey on Machine Learning Based Routing Algorithms[J]. Journal of Computer Research and Development, 2020, 57(4): 671-687. DOI: 10.7544/issn1000-1239.2020.20190866 |
[8] | Liu Junxu, Meng Xiaofeng. Survey on Privacy-Preserving Machine Learning[J]. Journal of Computer Research and Development, 2020, 57(2): 346-362. DOI: 10.7544/issn1000-1239.2020.20190455 |
[9] | Xu Xiaoxiang, Li Fanzhang, Zhang Li, Zhang Zhao. The Category Representation of Machine Learning Algorithm[J]. Journal of Computer Research and Development, 2017, 54(11): 2567-2575. DOI: 10.7544/issn1000-1239.2017.20160350 |
[10] | Wen Guihua. Relative Transformation for Machine Learning[J]. Journal of Computer Research and Development, 2008, 45(4): 612-618. |