Kiran Venugopal

Interference in finite-sized highly dense millimeter wave networks

The potential of millimeter wave (mmWave) frequencies for device-to-device communication among wearable electronics is enormous for applications requiring Gbps through-put. In a dense usage scenario such as inside a train car or airplane cabin, many devices may be present within close proximity where interference is significant. Previous work that models interference in wireless networks has leveraged stochastic geometry and often assumes an infinite number of interferers. In the indoor wearables setting, a finite network may be a more realistic assumption. This paper analyzes mmWave networks with a finite number of interferers that are located in fixed positions. The paper considers the effect of blockages, which are primarily caused by the human bodies present in the operating environment. Expressions for coverage and rate are developed, which capture the effects of key antenna characteristics such as directivity and gain.

Device-to-Device Millimeter Wave Communications: Interference, Coverage, Rate, and Finite Topologies

Emerging applications involving device-to-device communication among wearable electronics require gigabits per second throughput, which can be achieved by utilizing millimeter-wave (mmWave) frequency bands. When many such communicating devices are indoors in close proximity, such as in a train, car, or airplane cabin, interference can be a serious impairment. This paper uses stochastic geometry to analyze the performance of mmWave networks with a finite number of interferers in a finite network region. Prior work considered either lower carrier frequencies with different antenna and channel assumptions, or a network with an infinite spatial extent. In this paper, human users not only carry potentially interfering devices, but also act to block interfering signals. Using a sequence of simplifying assumptions, accurate expressions for coverage and rate are developed that capture the effects of key antenna characteristics, such as directivity and gain, and are a function of the finite area and number of users. The assumptions are validated through a combination of analysis and simulation. The main conclusions are that mmWave frequencies can provide gigabits per second throughput even with omni-directional transceiver antennas, and larger, more directive antenna arrays give better system performance.

Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.

Millimeter Wave Networked Wearables in Dense Indoor Environments

Supporting high data rate wireless connectivity among wearable devices in a dense indoor environment is challenging. This is primarily due to bandwidth scarcity when many users operate multiple devices simultaneously. The millimeter-wave (mmWave) band has the potential to address this bottleneck, thanks to more spectrum and less interference because of signal blockage at these frequencies. In this paper, we explain the potential and challenges associated with using mmWave for wearable networks. To provide a means for concrete analysis, we present a system model that admits easy analysis of dense, indoor mmWave wearable networks. We evaluate the performance of the system while considering the unique propagation features at mmWave frequencies, such as human body blockages and reflections from walls. One conclusion is that the non-isotropy of the surroundings relative to a reference user causes variations in system performance depending on the user location, body orientation, and density of the network. The impact of using antenna arrays is quantified through analytic closed-form expressions that incorporate antenna gain and directivity. It is shown that using directional antennas, positioning the transceiver devices appropriately, and orienting the human user body in certain directions depending on the user location result in gigabits-per-second achievable ergodic rates for mmWave wearable networks.

Analysis of millimeter wave networked wearables in crowded environments

The millimeter wave (mmWave) band has the potential to provide high throughput among wearable devices. When mmWave wearable networks are used in crowded environments, such as on a bus or train, antenna directivity and orientation hold the key to achieving Gbps rates. Previous work using stochastic geometry often assumes an infinite number of interfering nodes drawn from a Poisson Point Process (PPP). Since indoor wearable networks will be isolated due to walls, a network with a finite number of nodes may be a more suitable model. In this paper, we characterize the significant sources of interference and develop closed-form expressions for the spatially averaged performance of a typical users wearable communication link. The effect of human body blockage on the mmWave signals and the role of network density are investigated to show that an increase in interferer density reduces the mean number of significant interferers.

Time-domain channel estimation for wideband millimeter wave systems with hybrid architecture

Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for hybrid architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.

Analysis of Urban Millimeter Wave Microcellular Networks

Millimeter wave (mmWave) networks are sensitive to blockages due to buildings in urban areas. This is critical for vehicle-to-infrastructure networks which are cellular networks designed to support emerging vehicular applications. Motivated by measurement and ray tracing results in urban microcells, instead of characterizing the pathloss by Euclidean distance, we calculate it by the weighted sum of segment length along the propagation path, i.e., Manhattan distance, and a certain corner loss at the intersections along the path. We analyze network performance by modeling the urban microcell network by a Manhattan Poisson line process. Our results show significant differences between Manhattan and Euclidean distance- based pathloss models. Assuming the receiver is associated with the base station (BS) with the smallest pathloss, we derive closed-form expression of the distribution of the associated link pathloss. We obtain the coverage probability and reveal the impacts of interference from the LOS and NLOS BSs. It is shown that in this scenario the interference from a NLOS parallel street is negligible.

Location based performance model for indoor mmWave wearable communication

Simultaneous use of high-end wearable wireless devices like smart glasses is challenging in a dense indoor environment due to the high nature of interference. In this scenario, the millimeter wave (mmWave) band offers promising potential for achieving gigabits per second throughput. Here we propose a novel system model for analyzing system performance of mmWave based communication among wearables. The proposed model accounts for the non-isotropy of the indoor environment and the effects of reflections that are predominant for indoor mmWave signals. The effect of human body blockages are modeled and the system performance is shown to hugely vary depending on the user location, body orientation and the density of the network. Closed form expressions for spatially averaged signal to interference plus noise ratio distribution are also derived as a function of the location and orientation of a reference user.

A frequency-domain approach to wideband channel estimation in millimeter wave systems

Channel estimation allows millimeter wave (mmWave) MIMO communication systems to design pre-coders and combiners under different objective functions. Hybrid MIMO architectures provide a good trade-off power consumption-performance at mmWave frequencies, but most of the prior work on channel estimation for these structures assumes a narrowband channel model. In this paper, we propose a sparse approach for frequency selective channel estimation for mmWave channels, assuming a hybrid architecture. Simulation results show that the estimation error is small and the computational complexity is kept low. Moreover, the algorithm requires less training overhead than competing approaches based on beam training.

Blockage and Coverage Analysis with MmWave Cross Street BSs Near Urban Intersections

Millimeter wave (mmWave) communication offers Gbps data transmission, which can support massive data sharing in vehicle-to-infrastructure (V2I) networks. In this paper, we analyze the blockage effects among different vehicles and coverage probability of a typical receiver, considering cross street BSs near urban intersections in a multi-lane mmWave vehicular network. First, a three-dimensional model of blockage among vehicles on different lanes is considered. Second, we compute the coverage probability considering the interference of cross street base stations. Incorporating the blockage effects, we derive an exact and semi closed-form expression of the cumulative distribution density (CDF) of the association link path gain. Then, a tight approximation of the coverage probability is computed. We provide numerical results to verify the accuracy of the analytic results. We demonstrate the effects of blockage and the cross street interference. Also, we compare coverage probability with different BSs intensities under various street settings. It is shown that in multi-lane V2I networks, blockage among vehicles is not significant. Also, deploying more BSs does not increase coverage probability efficiently in ultra-dense streets.