Spectrum Sharing:


Wireless spectrum resources are becoming gradually scarce with the dramatically increasing number of mobile devices in wireless networks. 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 communication channels. 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 co-channel ground communication links. Without a dedicated spectrum, researchers need to design efficient spectrum-sharing policies for UAV communications to enhance area spectral efficiency (ASE) 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 primary ground networks.

LTE-Unlicensed (LTE-U) is considered a groundbreaking technology to address the increasing scarcity of available spectrum by extending cellular communications to the unlicensed band. In [6], we investigated the performance of sidelink communications in conjunction with LTE-U, which can alleviate the traffic load of cellular networks. However, in the same unlicensed band, the coexistence of sidelink and WiFi technologies should be carefully designed to satisfy the user's QoS and avoid severe interferences and contentions among devices using the unlicensed spectrum.





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.

6. 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.

7. M. Liu, J. Zhang, Y. Lin, Z. Wu, B. Shang, and F. Gong, "Carrier Frequency Estimation of Time-Frequency Overlapped MASK Signals for Underlay Cognitive Radio Network," in IEEE Access (ACCESS), vol. 7, pp. 58277-58285, 2019.







Task Offloading:


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, base stations (BSs) are suffering heavy load burdens. Sidelink 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 systems. 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 content sharing.

In [1] and [2], we studied an economic aspect of traffic offloading via content sharing among multiple devices and proposed an incentive framework for device-to-device (D2D)-assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency, by encouraging users to act as D2D transmitters (D2D-Txs), which broadcast their popular contents to nearby users.

In [3] and [4], we modeled and analyzed the wireless-powered D2D-assisted offloading in cellular networks, where the D2D-Txs can harvest radio frequency energy from nearby BSs and utilize the harvested energy to share popular contents with nearby user equipments. We characterized the intrinsic relationship between the wireless power transfer and the information transmission. In [5], we evaluated the energy efficiency of the heterogeneous cellular networks.

In [6], we studied an energy-efficient computation offloading for vehicular edge computing systems, where multiple roadside units (RSUs) assist vehicular users in offloading their computation tasks to edge servers. Our goal was to minimize the users’ energy consumption by optimizing users' association, data partition, transmit power, and computation resource allocation, subject to the constraints of partial tasks offloading, users' latency, maximum transmit power, outage performance, and computation capacity of edge servers.





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, 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.

6. 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.

7. 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.

8. H. Zhang, Y. Yang, B. Shang, and P. Zhang, “Joint Resource Allocation and Multi-Part Collaborative Task Offloading in MEC Systems”, in IEEE Transactions on Vehicular Technology (TVT), 2022.

9. J. Ma, B. Shang, H. Song, Y. Huang and P. Fan, "Reliability Versus Latency in IIoT Visual Applications: A Scalable Task Offloading Framework," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2022.3148115.

10. L. Liu, J. Feng, Q. Pei, C. Chen, Y. Ming, B. Shang, and M. Dong, “Blockchain-enabled Secure Data Sharing Scheme in Mobile Edge Computing: An Asynchronous Advantage Actor-Critic Learning Approach”, in IEEE Internet of Things Journal (IOTJ), vol. 8, no. 4, pp. 2342-2353, 15 Feb.15, 2021.

11. J. Feng, Q. Pei, F. R. Yu, X. Chu, and B. Shang, "Computation Offloading and Resource Allocation for Wireless Powered Mobile Edge Computing with Latency Constraint", in IEEE Wireless Communications Letters (WCL), vol. 8, no. 5, pp. 1320-1323, Oct 2019.







UAV Swarm:


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 [1]. A UAV swarm is a set of drones that work together to achieve a specific goal. A common purpose for drones is a military one, but their civilian applications are attracting increased attention in recent times.

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. Despite the ground RIS deployment, the UAV-enabled aerial RIS provides panoramic reflections for ground communications and can be efficiently deployed [2].

Compared to aerial RIS (ARIS), UAV swarm-enabled ARIS (SARIS) system has the following advantages. First, SARIS increases the aperture gain by increasing the number of UAVs. Second, SARIS guarantees UAV flight stability and flexibility by allowing moderate-sized RIS on each UAV, especially under bad weather conditions or air turbulence. Third, SARIS supports spatial multiplexing for a large number of users by providing a rich scattering environment with different UAVs’ positions. Forth, with the reduced RIS size on each UAV, the production cost of RIS can be decreased, and the flight time of the UAV can be prolonged. In [3], we studied a SARIS system, where multiple UAVs mounted with RIS assist the downlink transmissions between a base station (BS) and ground users. We optimized the beamforming and placement design for the SARIS system.

In [4], we studied the trade-offs in reliability, latency, and energy in random network coding-enabled UAV swarm networks. We aimed to reduce the weighted sum of latency and energy while guaranteeing a targeted successful transmission probability, which is defined as the probability that the RNC-enabled UAV swarm network has sufficient generations when the transmitter receives a required number of acknowledgments (ACKs) feedback.





1. 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.

2. 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.

3. B. Shang, E. Bentley, and L. Liu, "UAV Swarm-Enabled Aerial Reconfigurable Intelligent Surface: Modeling, Analysis, and Optimization", in IEEE Transactions on Communications (TCOM), 2022.

4. 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.

5. H. Song, L. Liu, B. Shang, S. Pudlewski and E. S. Bentley, "Enhanced Flooding-Based Routing Protocol for Swarm UAV Networks: Random Network Coding Meets Clustering," IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1-10.







Space-Air-Ground Integrated Networks:


Space-air-ground integrated networks (SAGINs) have gained significant attention and become a promising architecture for ubiquitous connectivity for 5G-Advanced and 6G, enabling the integration of satellite networks, aerial networks, and terrestrial networks. This integration brings tremendous communication benefits, such as non-terrestrial networks, seamless global coverage, high flexibility, and augmented system capacity. Meanwhile, computing capability becomes an indispensable part of the SAGIN ecosystem. In SAGINs, limited and unbalanced computation and communication resources of different network segments make it challenging to provide strict quality-of-service (QoS) guarantees for specific traffic (e.g., delay-sensitive traffic and outage-sensitive traffic).

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 cooperative computing in space-air-ground integrated networks.

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 [2], 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 [3], we introduced UAV-enabled aerial RIS (ARIS) technology to MEC. Specifically, an RIS is mounted on a UAV instead of the edge server to reflect users’ signals to the BS for computation offloading. We compared different computation offloading schemes, i.e., ARIS-assisted MEC, MEC without UAV, and UAV-enabled MEC.





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, 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.

3. B. Shang, H. V. Poor, and L. Liu, "Aerial Reconfigurable Intelligent Surfaces Meet Mobile Edge Computing", in IEEE Wireless Communications (WCM), 2022.