Ehsan Aryafar

Emerging mobile applications give rise to new bandwidth-hungry and latency-sensitive traffic classes that challenge existing wireless systems. Addressing them requires innovative approaches such as simultaneous data transmission across multiple Radio Access Technologies (RATs), e.g., WiFi and WiGig. However, existing transport layer multi-RAT traffic aggregation schemes, e.g., multi-path TCP, suffer from Head-of-Line (HoL) blocking and sub-optimal traffic splitting across the RATs that severely penalize their performance. In this paper, we investigate the design of FBDT, a novel multi-path transport layer solution that for the first time can achieve the sum of the throughput rates across the individual RATs network paths, despite their channel conditions’ dynamics. We have implemented FBDT in the Linux kernel and show substantial improvement in throughput relative to state-of-the-art schemes, e.g, 2.5x gain in a dual-RAT scenario (WiFi and WiGig) when the client is mobile. Second, we extend FBDT to more than two radios and demonstrate that its throughput performance scales linearly with the number of RATs, in contrast to multi-path TCP, whose performance degrades with an increase in the number of RATs. We evaluate the performance of FBDT on different traffic classes and demonstrate: (i) 2-3 times shorter file download times, (ii) up to 10 times shorter streaming times and 10 dB higher video quality for progressive download video applications, and (iii) up to 9 dB higher viewport quality for interactive mobile VR applications, when our viewport quality maximization framework is employed along with FBDT.

This paper proposes a full-duplex (FD) antenna design with passive self-interference (SI) suppression for the 28 GHz mmWave band. The reduction in SI is achieved through the design of a novel configuration of stacked Electromagnetic Band Gap structures (EBGs), which create a high impedance path to travelling electromagnetic waves between the transmit and receive antenna elements. The EBG is composed of stacked patches on layers 1 and 2 of a four-layer stack-up configuration. We present the design, optimization, and prototyping of unit antenna elements, stacked EBGs, and integration of stacked EBGs with antenna elements. We also evaluate the design through both HFSS (High Frequency Structure Simulator) and over-the-air measurements in an anechoic chamber. Through extensive evaluations, we show that (i) compared to an architecture that does not use EBGs, the proposed novel stacked EBG design provides an average of 25 dB of additional reduction in SI over 1 GHz of bandwidth, (ii) unit antenna element has over 1 GHz of bandwidth at −10 dB return loss, and (iii) HFSS simulations show close correlation with actual measurement results; however, measured results could still be several dB lower or higher than predicted simulation results. For example, the gap between simulated and measured antenna gains is less than 1 dB for 26–28 GHz and 28.5–30 GHz frequencies, but almost 3 dB for 28–28.5 GHz frequency band.

Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To address the problem, we developed a Gated Recurrent Unit (GRU) model that is trained using periodically exchanged messages in mmWave systems. We gathered extensive amount of simulation data from a commercially available mmWave simulator, show that the proposed method does not incur any additional communication overhead, and that it achieves outstanding results in selecting the optimal blockage mitigation method with an accuracy higher than 93%. We also show that the proposed method significantly increases the amount of transferred data compared to several other blockage mitigation policies.

In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems.

Full-duplex (FD) communication in many-antenna base stations (BSs) is hampered by self-interference (SI). This is because a FD node’s transmitting signal generates significant interference to its own receiver. Recent works have shown that it is possible to reduce/eliminate this SI in fully digital many-antenna systems, e.g., through transmit beamforming by using some spatial degrees of freedom to reduce SI instead of increasing the beamforming gain. On a parallel front, hybrid beamforming has recently emerged as a radio architecture that uses multiple antennas per FR chain. This can significantly reduce the cost of the end device (e.g., BS) but may also reduce the capacity or SI reduction gains of a fully digital radio system. This is because a fully digital radio architecture can change both the amplitude and phase of the wireless signal and send different data streams from each antenna element. Our goal in this paper is to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity, particularly in multi-user MIMO setups. To do so, we experimentally compare the performance of a state-of-the-art fully digital many antenna FD solution to a hybrid beamforming architecture and compare the corresponding performance metrics leveraging a fully programmable many-antenna testbed and collecting over-the-air wireless channel data. We show that SI cancellation through beam design on a hybrid beamforming radio architecture can achieve capacity within 16% of that of a fully digital architecture. The performance gap further shrinks with a higher number of quantization bits in the hybrid beamforming system.

Data generated by increasingly pervasive and intelligent devices has led to an explosion in the use of machine learning (ML) and artificial intelligence, with ever more complex models trained to support applications in fields as diverse as healthcare, finance, and robotics. In order to train these models in a reasonable amount of time, the training is often distributed among multiple machines. However, paying for these machines (either by constructing a local cloud infrastructure or renting machines through an external provider such as Amazon AWS) is very costly. We propose to reduce these costs by creating a marketplace of computing resources designed to support distributed machine learning algorithms. Through our marketplace (coined “DeepMarket”), users can lend their spare computing resources (when not needed) or augment their resources with available DeepMarket machines to train their ML models. Such a marketplace directly provides several benefits for two groups of researchers: (i) ML researchers would be able to train their models with much reduced cost, and (ii) network economics researchers would be able to experiment with different compute pricing mechanisms. The focus of this Demo is to introduce the audience to DeepMarket and its user interface (named “PLUTO”). In particular, we will bring a few laptops with pre-installed PLUTO applications so that users can see how they can create an account on DeepMarket servers, lend their resource, borrow available resources, submit ML jobs, and retrieve the results. Our overall goal is to encourage the conference audience to install PLUTO on their own machines and create a user and developer community around DeepMarket.