Research

Optimization and communication in Non-terrestrial Networks

Drones, also known as unmanned aerial vehicles (UAVs), are becoming a promising solution for a wide range of applications in current and upcoming cellular networks. As a new type of users, UAVs are increasingly being used in wireless networks. UAVs are also being used as a new type of base stations (BSs). The fact that they are able to move, makes UAVs a viable solution as flying BSs to cover e to hard-to-reach areas. More so, their flexibility, agility, adaptability, and cost-effectiveness make them an attractive alternative to the conventional rooftop or pole-mounted BSs, at least in certain situations. In addition to aerial users or BSs, UAVs can be exploited as relays. In addition to UAVs, the use of Low Earth Orbit (LEO) small-satellite constellation can support ultra-reliable communications (URL) with relaxed latency requirements of a few tens of milliseconds. These constellations are deployed at alititudes between 300-2000 km and their integration with 5G New Radio will provide a global coverage. This shift has been motivated by two main factors: 1) the increasing demand for higher capacity in the already covered areas, and 2) the need to extend the broadband coverage to under-served areas. Such new paradigms bring new opportunities but also stress the current cellular networks in various ways. For instance, the SpaceX's project, Starlink, is set to place 42,000 satellites at an altitude of 340-570 km to provide complete global coverage. To this end, our main focus is the integration of UAVs and LEO satellite constellations in next-generation cellular communication networks. This will open the new innovation opportunites for Beyond-5G and 6G networks.

Research topics are listed as follows:

1) Antenna design for time-varying LoS MIMO links

2) Rate splitting multiple access for multi-beam multicast networks

3) LEO satellite topological interference managment

New Multiple Access for Massive Connectivity

Future radio access networks are expected to support a massive number of users with a diverse set of requirements in terms of delay, throughput, etc. These requirements challenge different aspects of the current cellular networks including the multiple access (MA) methods. A common feature of newly designed MA schemes is the use of non-orthogonal multiple access (NOMA) schemes instead of the conventional orthogonal schemes. NOMA is a potential enabler for the development of beyond 5G wireless networks. Compared to the conventional orthogonal multiple access (OMA) schemes such as TDMA and FDMA, NOMA can scale up the number of served users, increase the spectral efficiency, and improve user-fairness. In particular, NOMA has been suggested as a possibility for data transmission in dense networks with a large number of users requesting for access such that there are not enough orthogonal resources to serve them in a OMA-based fashion. As such, NOMA has been proposed for the 3rd Generation Partnership Project (3GPP) Long Term Evolution–Advanced (LTE–A) standards and is envisioned to be a part of 5G cellular networks. Nonetheless, while single-cell NOMA has drawn significant attention recently, much less attention has been given to multi-cell NOMA.

Our research goal is to investigate the potentials and challenges of NOMA in a multi-cell environment and to harvest the benefits of NOMA. Inter-cell interference, particularly at the cell-edge, is by far the main challenge in multi-cell networks. This interference situation becomes worse when NOMA is used, as cell-edge users constantly experience interference from the neighboring cell, whereas in the case of TDMA/FDMA interference is limited to certain time slots or frequency bands. To deal with this problem in a MIMO communication network, we have developed two new interfere alignment (IA)-based technique in. This approach is extended to an arbitrary number of cells, where the maximum number of users supported by the proposed scheme in multi-cell MIMO networks is characterized too.

Signal Processing for Grant-free NOMA

Massive IoT networks requires ultra-responsive and ultra-reliable connections. As a promising enable of the massive IoT, grant-free NOMA exploits the joint benefit of grant-free access and nonorthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. We formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we can resort to deep learning by parameterizing the interactable variational function with a specially designed deep neural network.

Main challenges:

1. Determining which devices can be active at all times

2. Decoding the information bits that are transmitting over the air