Research

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Cooperative Communication and Sensing with UAV Swarms

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Utilizations of unmanned aerial vehicles (UAV) for both commercial and military sectors has recently emerged as promising technology. Due the ease of deployment and high manoeuvrability, UAVs are used in many applications, like environmental monitoring, disaster recovery, aerial photography, and aerial cellular data access. Specifically, we focus on the distributed radar imaging, beamforming and multiple-input multiple-output (MIMO) communication enabled by swarms.

In this project, we are developing several systems that utilize a swarm of cooperative UAVs to improve the communication and sensing capabilities. The ongoing projects are focusing on the following research challenges for distributed swarm communication: 1) Algorithm, protocol, and software defined radio implementation of timing and frequency synchronization among UAVs; 2) Novel distributed beamforming and MIMO processing at the UAV edge with reduced backhaul capacity requirements; 3) Optimal placement of UAVs that achieves a maximal cooperative communication throughput and its distributed implementation. For sensing applications, placement of UAVs provides more degree of freedom in the attempt to improve the image quality in radars and reduce the reconstruction errors.

Staff

Selected publications

Millimeter-Wave Massive MIMO Mobile Network: Physical Layer Perspective and Hardware Impairments

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Millimeter-wave (mmW) wireless communication will be a key component in the future cellular networks (5G and beyond). The mmW system has unique characteristics as compared to conventional communication system. It features massive antenna array in both base station and user equipment, and communication channel has sparse scattering. Therefore, many physical layer procedures from current networks will dramatically change. Besides, a rethink in transceiver hardware architecture and impact from hardware impairments are necessary in system with an order of magnitude higher carrier frequency, processing bandwidth, and number of antenna elements. In this study, we have two goals:

1) We provide an understanding of how unique features in mmW communication (e.g., channel sparsity, large antenna array, etc.) affect system performance in physical layer procedures including initial access, transceiver synchronization, channel tracking, beamforming and multiplexing. All the above studies are closely aligned with modeling power consumption and hardware impairment in practical mmW transceivers.

2) We develop and analyze a novel DSP algorithm to improve performance of above physical layer procedures and save hardware power consumption and cost in mmW networks.


Staff

Selected publications

Millimeter-Wave Massive MIMO Mobile Network: Beam Association and Handover in Ultra-Dense Network

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In millimeter wave networks, a high density of base stations is required to avoid outages resulting from the propagation loss and signal blockages. In order to compensate for the signal losses, it has been proposed to equip mmW transmitters and receivers with large number of antennas, which enables forming of narrow beams with high beamforming gains. However, it is not feasible to provide one RF chain per antenna due to high cost of hardware, especially ADC. In order to reduce the hardware cost, a hybrid analog and digital beamforming architecture has been proposed where the number of RF chains, K, is orders of magnitude smaller than the total number of antennas N. A BS can form only K independent beams and serve K UEs at any given instant. Therefore, providing sufficient data rates for a large number of users becomes a challenge and requires judicious allocation of BS resources to the associated UEs. Further, a dense deployment of base stations may result in frequent handovers which impedes seamless data transfer. We address these challenges in the following tasks

1) Beam association and resource allocation (BA/RA): In this task, we will develop an algorithm to associate UE beams to BS beams. We exploit the fact that in UDN one UE can be associated with and receive data from multiple BSs in its vicinity using multiple RF chains. The proposed algorithm allocates time domain resources at BS to associated UE beams and optimizes the beam widths at UEs and BSs to provide robustness against beam alignment errors. The objective of this task is to serve the maximum number of UEs in the network with required data rates.

2) Mobility management and handover: In this task, we propose a two step handover procedure consisting of link update and joint BA/RA with handover. In the first step, we update the link parameters (channel gain, AoA, and AoD) for links with low received SNR. The proposed link update procedure requires lower signaling overhead than existing methods. In the second step, the BA/RA and handover problem is jointly solved in order to reduce the number of handovers by optimizing beam widths at UEs and BSs.

Staff

Selected publications

Project page

Visit the dedicated page for more details.

Massive MIMO in Interference Management in Cognitive Radio Networks

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In coming years, wireless networks have to serve a large number of data hungry devices due to proliferation of new devices and applications every year. Increasing number of wireless devices (users) are competing for physical layer resources, namely time, space and frequency, to download the data required for different applications. In order to serve a large number of users, the wireless network needs to either create new resources or utilize existing resources more efficiently.

We propose to create spatial resources in the channel by using massive MIMO technique in a cognitive cell. The cognitive cell coexists with the primary cell in underlay paradigm. In underlay CR network, the cognitive base station (CBS) can serve CRs if the interference to the primary users (PUs) is kept below a threshold. By using a large antenna array at CBS, cognitive cell simultaneously serves a large number of CRs, while restricting the interference to PUs below a specified threshold. In this project, we investigate how the network parameters, namely number of antennas at CBS (Mb), number of antennas at CRs (Mu), and number of PUs affect the ability of CBS to serve a large number of CRs. We consider three constraints in this study: 1) maximum interference at PU (I0), 2) minimum rate required by each SU (R0) , and 3) maximum transmit power (P0). The imperfect knowledge of the channel between CBS and PU is assumed in our approach.

Staff

Selected publications

Load Balancing and Interference Management for Heterogeneous Cellular Networks

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With the proliferation of wireless devices and the advent of bandwidth hungry applications such as video streaming, cloud-based technologies, etc., incremental improvements to existing networks and architectures such as 4G are insufficient to meet the projected data demands in the next five years. This has called for paradigm shifts in next generation networks, i.e., 5G, and particularly, the ongoing evolution towards very dense and unplanned deployment of low-power small cell base stations (BSs) of various types, commonly known as HetNets.

Small cells can significantly enhance network’s throughput, but there are several key challenges associated with the deployment of HetNets such as load-balancing and the high interference. For instance, low-power BSs can be overshadowed by the high transmit power of the macro BS, rendering existing user association techniques such as the max-power user association impractical due to the load-imbalance it causes in the network. Similarly, co-channel deployment of these cells prohibitively increases the interference, degrading the quality-of-service especially for users located at cell edges.

We propose load-aware user association policies that significantly improve the user’s throughput by balancing the load for different network settings. In addition, we study interference not only via the allocation of time-frequency resources but also via the allocation of spatial resources based on the premise that massive MIMO will be an integral technology in 5G.

Staff

Selected publications

Cooperatively Learning Locations and Footprints of Multiple Coexisting Primary Users

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This project focuses on developing cooperative algorithms for the identification of spectrum holes and classification of primary network activity by geographically large-scale cognitive radio networks. The project consists of three parts, namely localization, footprint learning and network topology learning.

The knowledge of PU location is required in a cognitive radio (CR) network for advanced spectrum sharing, e.g., location-based interference management. Our proposed Cyclic Weighted Centroid Localization (WCL) algorithm uses the distinct cyclostationary properties of the PUs in order to estimate the location of each. The proposed Cyclic WCL reduces the localization error by 5x as compared to traditional WCL when the interferer's transmit power is 40 dB higher than the target.

When multiple CRs and PUs coexist in the same spectrum, different sets of CRs may receive signals from each PU. Learning these footprints of each PU is necessary for disambiguating the PU signals which, in turn, is necessary for computing individual PUs’ radio environment map, location, and for signal classification. To increase versatility of our proposed algorithms, we use only the received energy measurements at the CRs as input. Furthermore, they do not rely on knowledge of the channel propagation models or the location of any radios, PUs or CRs. Their ability to distinguish the signals from each PUs can be used to extend existing algorithms, such as WCL, designed for single PU systems.

The learned footprints and the detected spectrum occupancy will be used to analyze the PU activity over time. The PU activity will be used to identify sets of PUs that are part of the same network. Learning the network topology will improve the spatial resolution of the spectrum holes by identifying the receiver of each PU transmission.

Staff

Selected publications

Project page

Visit the dedicated page for more details.

Previous Projects

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