1. Algorithm Design for Wireless Communications:

 

 

a) Non-Terrestrial Networks:

 

As unmanned aerial vehicles (UAVs) and satellites become more available, wireless networks will not be restricted to terrestrial networks. There are many applications for UAVs in wireless networks, such as UAV swarm networks in disasters, UAV-assisted vehicle-to-everything (V2X) communications, UAV-enabled smart city development, traffic offloading in hotspots, and surveillance and Internet of Things (IoT) networks. The space network performs as a vital tool to support global information exchange, especially serving as a “last resort” for communicating in remote areas. The wireless network architecture will become a 3D structure, incorporating terrestrial and aerial and space network nodes, which are more dynamic than the fixed terrestrial communications network. In [1], we investigated the cooperative computing in space-air-ground integrated networks. In [2], we investigated the spectrum sharing for UAV communications. In [3], we studied the physical layer security with unmanned aerial systems. In [4], we designed a resource allocation algorithm for UAV-enabled mobile edge computing (MEC). In [5], we analyzed and optimized the spectrum sharing between UAV and device-to-device (D2D) hybrid networks. In [6], we studied the trade-offs in reliability, latency, and energy in random network coding-enabled UAV swarm networks.

 

 

References:

1. B. Shang, Y. Yi, and L. Liu, "Computing over Space-Air-Ground Integrated Networks: Challenges and Opportunities", in IEEE Network (NETWORK), vol. 35, no. 4, pp. 302-309, July/August 2021.

2. B. Shang, V. Marojevic, Y. Yi, A. Abdalla, and L. Liu, "Spectrum Sharing for UAV Communications: Spatial Spectrum Sensing and Open Issues", in IEEE Vehicular Technology Magazine (VTM), vol. 15, no. 2, pp. 104-112, June 2020.

3. B. Shang, L. Liu, J. Ma, and P. Fan, "Unmanned Aerial Vehicle Meets Vehicle-to-Everything in Secure Communications", in IEEE Communications Magazine (COMMAG), vol. 57, no. 10, pp. 98-103, Oct 2019.

4. B. Shang, and L. Liu, "Mobile Edge Computing in the Sky: Energy Optimization for Air-Ground Integrated Networks", in IEEE Internet of Things Journal (JIOT), vol. 7, no. 8, pp. 7443-7456, Aug. 2020.

5. B. Shang, L, Liu, R. M. Rao, V. Marojevic, and J. H. Reed, "3D Spectrum Sharing for Hybrid D2D and UAV Networks", in IEEE Transactions on Communications (TCOM), vol. 68, no. 9, pp. 5375-5389, Sept. 2020.

6. B. Shang, L. Liu, H. Song, B. Xu, S. Pudlewski, and E. Bentley, “Trade-offs in Reliability, Latency, and Energy for Random Network Coding-Enabled Networks”, in IEEE Communications Letters (CL), vol. 25, no. 8, pp. 2768-2772, Aug. 2021.

7. B. Shang, R. Shafin, and L. Liu, "UAV Swarm Enabled Aerial Reconfigurable Intelligent Surface (SARIS)", in IEEE Wireless Communications (WCM), 2021, doi: 10.1109/MWC.010.2000526.

 

 

 

b) Edge Computing:

 

Mobile edge computing (MEC) allows mobile user equipment (UE) to offload computation tasks onto network edges such as cellular base stations, rather than computing locally, to reduce the computation latency and mobile device's energy consumption. Unlike popular cloud computing, offloading computation tasks onto MEC servers in proximity can reduce the transmission delay. However, the computation capacity of a MEC server is generally limited. In [1], we developed an energy-efficient joint communication and computation resource allocation scheme, as well as UAV placement, for air-ground integrated MEC networks. Specifically, the total energy consumption at UEs in air-ground integrated MEC networks was minimized, by jointly optimizing users' association, uplink power control, channel allocation, computation capacity allocation, and UAVs' 3D placement, under the constraints of binary offloading, UEs' latency, computation capacity, UAV energy consumption, and available bandwidth. In [2], we studied an algorithm for energy-efficient resource allocation in the vehicular edge computing (VEC) system, where multiple roadside units assisted users in offloading computation tasks to edge servers.

 

 

References:

1. B. Shang, and L. Liu, "Mobile Edge Computing in the Sky: Energy Optimization for Air-Ground Integrated Networks", in IEEE Internet of Things Journal (JIOT), vol. 7, no. 8, pp. 7443-7456, Aug. 2020.

2. B. Shang, L. Liu, and Z. Tian, "Deep Learning-Assisted Energy-Efficient Task Offloading in Vehicular Edge Computing Systems", in IEEE Transactions on Vehicular Technology (TVT), vol. 70, no. 9, pp. 9619-9624, Sept. 2021.

3. B. Shang, S. Liu, S. Lu, Y. Yi, W. Shi, and L. Liu, "A Cross-Layer Optimization Framework for Distributed Computing in IoT Networks", in Workshop on Edge Computing and Communications (EdgeComm) of 2020 IEEE/ACM Symposium on Edge Computing (SEC), San Jose, CA, USA, 2020, pp. 440-444.

4. B. Shang, Y. Yi, and L. Liu, "Computing over Space-Air-Ground Integrated Networks: Challenges and Opportunities", in IEEE Network (NETWORK), vol. 35, no. 4, pp. 302-309, July/August 2021.

 

 

 

c) Reconfigurable Intelligent Surface:

 

Reconfigurable intelligent surface (RIS), which is composed of passive reflecting elements, can strength the signals at receivers and reduce the interference by designing optimized reflector coefficients. Due to the passive nature of RIS, it enjoys low power consumption without active transmitters at RIS. It is envisioned that deploying RIS in future wireless systems achieves energy efficient wireless communications and can improve wireless network performance by increasing the number of reflectors at RIS. Despite the ground RIS deployment, the UAV-enabled aerial RIS provides panoramic reflections for ground communications and can be efficiently deployed. In this work, we analyze and design the UAV swarm-enabled aerial RIS for ground users' communications which achieves the considerable reflection gain by UAV swarm.

 

 

References:

1. B. Shang, R. Shafin, and L. Liu, "UAV Swarm Enabled Aerial Reconfigurable Intelligent Surface (SARIS)", in IEEE Wireless Communications (WCM), 2021, doi: 10.1109/MWC.010.2000526.

2. B. Shang, E. Bentley, and L. Liu, "UAV Swarm-Enabled Aerial Reconfigurable Intelligent Surface: Modeling, Analysis, and Optimization", submitted to IEEE for possible publication.

3. B. Shang, and L. Liu, "Aerial Reconfigurable Intelligent Surface Meets Mobile Edge Computing", preparing.

 

 

 

 

 

2. Performance Analysis for Wireless Networks:

 

 

a) Spectrum Sharing:

 

With the dramatically increasing number of mobile devices in wireless networks, wireless spectrum resources are becoming gradually scarce. By reusing the licensed spectrum, proximate mobile devices can communicate directly without needing the data transmissions to go through network infrastructures, improving the overall system performance due to the reliable short communicating distances. However, this may create severe interference to cellular networks. Spatial spectrum sensing (SSS) motivates mobile devices to sense the spatial spectrum opportunities and reuse the scarce spectrum aggressively in cognitive radio networks. The interference generated from secondary users (SUs) to cellular networks can be managed to guarantee the Quality-of-Service (QoS) of cellular communications. In [1] and [2], the SSS-based SUs in uplink two-tier user-centric deployed HetNets were investigated. In [3], we developed a machine learning-based approach to characterize the aggregated received power distributions at a SU during SSS.

UAVs attract increasing attention for applications such as video streaming, surveillance, and delivery using reliable line-of-sight (LOS) links. Nevertheless, due to the large radio-frequency (RF) transmission footprint from a UAV transmitted to ground nodes, UAV communications may significantly deteriorate the performance of cochannel ground communication links. With the lack of a dedicated spectrum, researchers need to design efficient spectrum-sharing policies for UAV communications to enhance spectral efficiency (SE) and control interference-to-ground communications. In [4] and [5], we investigated the spectrum sharing for UAV communications. The optimal SSS radius for UAVs was obtained to maximize the ASE of UAV networks while guaranteeing the ASE of ground primary networks.

 

 

References:

1. B. Shang, L. Liu, H. Chen, J. C. Zhang, S. Pudlewski, E. S. Bentley, and J. D. Ashdown, “Spatial Spectrum Sensing in Uplink Two-Tier User-Centric Deployed HetNets”, in IEEE Transactions on Wireless Communications (TWC), vol. 19, no. 12, pp. 7957-7972, Dec. 2020.

2. B. Shang, L. Liu, H. Chen, J. C. Zhang, S. Pudlewski, E. S. Bentley, and J. D. Ashdown, “Spatial Spectrum Sensing-Based D2D Communications in User-Centric Deployed HetNets”, 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6.

3. B. Shang, and L. Liu, "Machine Learning Meets Point Process: Spatial Spectrum Sensing in User-Centric Networks", in IEEE Wireless Communications Letters (WCL), vol. 9, no. 1, pp. 34-37, Jan 2020.

4. B. Shang, L, Liu, R. M. Rao, V. Marojevic, and J. H. Reed, "3D Spectrum Sharing for Hybrid D2D and UAV Networks", in IEEE Transactions on Communications (TCOM), vol. 68, no. 9, pp. 5375-5389, Sept. 2020.

5. B. Shang, V. Marojevic, Y. Yi, A. Abdalla, and L. Liu, "Spectrum Sharing for UAV Communications: Spatial Spectrum Sensing and Open Issues", in IEEE Vehicular Technology Magazine (VTM), vol. 15, no. 2, pp. 104-112, June 2020.

 

 

 

b) Heterogeneous Cellular Networks:

 

With the upsurge of mobile data traffic and the explosive increase of mobile devices, cellular networks face technical challenges in supporting enormous data flows, high data rate, and large system capacity. In high user density areas, the base stations (BSs) are suffering heavy load burdens. Device-to-device (D2D) communications have been proposed to improve network capacity and alleviate the traffic burden on cellular networks by exploiting mobile devices' physical proximity. In the meantime, content sharing among multiple devices, e.g., video streaming, has been regarded as one of the most promising traffic offloading methods and a tremendous data-consuming application in wireless communications. However, as mobile devices are powered by limited battery energy, in general, there is no obligation for mobile devices to participate in cellular traffic offloading or D2D content sharing. In [1] and [2], we investigated the economic aspect of D2D-assisted offloading. In [3] and [4], we studied the wireless power transfer-enabled D2D-assisted offloading in cellular networks. Moreover, LTE-Unlicensed (LTE-U) is considered a groundbreaking technology to address the increasing scarcity of available spectrum by extending cellular communications to unlicensed bands. In [5], we analyzed the performance of the LTE-U-enabled direct link communications. In [6], we evaluated the energy efficiency of the integrated D2D and cellular networks.

 

 

References:

1. B. Shang, L. Zhao, K. C. Chen, and X. Chu, "An Economic Aspect of Device-to-Device Assisted Offloading in Cellular Networks," in IEEE Transactions on Wireless Communications (TWC), vol. 17, no. 4, pp. 2289-2304, April 2018.

2. B. Shang, L. Zhao, and K. C. Chen, "Operator's Economy of Device-to-Device Offloading in Underlaying Cellular Networks", in IEEE Communications Letters (CL), vol. 21, no. 4, pp. 865-868, April 2017.

3. B. Shang, L. Zhao, K. C. Chen, and X. Chu, "Wireless-Powered Device-to-Device Assisted Offloading in Cellular Networks", in IEEE Transactions on Green Communications and Networking (TGCN), vol. 2, no. 4, pp. 1012-1026, Dec 2018.

4. B. Shang, L. Zhao, K. C. Chen, and X. Chu, "Energy Efficient D2D-Assisted Offloading with Wireless Power Transfer", in 2017 IEEE Global Communications Conference (GLOBECOM), Singapore, 2017, pp. 1-6.

5. B. Shang, L. Zhao, and K. C. Chen, "Enabling device-to-device communications in LTE-unlicensed spectrum", 2017 IEEE International Conference on Communications (ICC), Paris, 2017, pp. 1-6.

6. B. Shang, L. Zhao, K. C. Chen, and G. Zhao, "Energy-Efficient Device-to-Device Communication in Cellular Networks", 2016 IEEE 83rd Vehicular Technology Conference (VTC) Spring, Nanjing, 2016, pp. 1-5.