Avishek Patra

Smart mm-Wave Beam Steering Algorithm for Fast Link Re-Establishment under Node Mobility in 60 GHz Indoor WLANs

Millimeter-wave (mm-wave) wireless local area networks (WLANs) are expected to provide multi-Gbps connectivity by exploiting the large amount of unoccupied spectrum in e.g. the unlicensed 60 GHz band. However, to overcome the high path loss inherent at these high frequencies, mm-wave networks must employ highly directional beamforming antennas, which makes link establishment and maintenance much more challenging than in traditional omnidirectional networks. In particular, maintaining connectivity under node mobility necessitates frequent re-steering of the transmit and receive antenna beams to re-establish a directional mm-wave link. A simple exhaustive sequential scanning to search for new feasible antenna sector pairs may introduce excessive delay, potentially disrupting communication and lowering the QoS. In this paper, we propose a smart beam steering algorithm for fast 60 GHz link re-establishment under node mobility, which uses knowledge of previous feasible sector pairs to narrow the sector search space, thereby reducing the associated latency overhead. We evaluate the performance of our algorithm in several representative indoor scenarios, based on detailed simulations of signal propagation in a 60 GHz WLAN in WinProp with realistic building materials. We study the effect of indoor layout, antenna sector beamwidth, node mobility pattern, and device orientation awareness. Our results show that the smart beam steering algorithm achieves a 7-fold reduction of the sector search space on average, which directly translates into lower 60 GHz link re-establishment latency. Our results also show that our fast search algorithm selects the near-optimal antenna sector pair for link re-establishment.

Packet scheduling for real-time communication over LTE systems

With various packet-switched networks coming to the fore, real-time services like voice and video, transmitted traditionally using circuit-switched bearers, can have limited capacity due to the limited availability of resource-granting control channels. Such packets are frequent and require more grants compared to other services like FTP. To compound the issue, often these packets are large in size compared to available resources for allocation. To improve the capacity of real-time communication over LTE (-A), various scheduling methods are being studied. However, often the packet sizes are unaccounted for by these studies. This work deals with the development of semi-persistent scheduling (SPS) algorithms based on wide-band time-average SINR information for resource allocation to voice traffic users, with a focus on large packets. A comparative study between dynamic scheduling (DS) and developed SPS algorithms is done to determine the suitable scheduling mechanism for voice packets transmission over LTE (-A) systems in the downlink.

Design and experimental evaluation of a 2.4 GHz-AoA-enhanced beamsteering algorithm for IEEE 802.11ad mm-wave WLANs

IEEE 802.11ad millimeter-wave (mm-wave) WLAN nodes achieve high throughput at the cost of the frequent re-steering requirement of highly directional antenna beams to establish and maintain links disrupted by beam misalignment, node mobility, or link blockage. Consequently, rapid and robust beamsteering algorithms are essential to enable seamless communication in mm-wave WLANs. In this paper we propose AoASteer, a beamsteering algorithm that speeds up the link establishment process in IEEE 802.11ad mm-wave WLANs by preferentially searching over a subset of mm-wave antenna sectors predicted by 2.4 GHz angle of arrival (AoA) estimation at the access point (AP). We experimentally evaluate the performance of AoASteer through extensive measurements in several indoor and outdoor locations, using 60 GHz USRP-SiversIMA packet radio transceivers to gather real mm-wave link information, and a 2.4 GHz USRP-based receiver with an 8-element uniform linear antenna array for AoA estimation, both implemented using GNU Radio. Our evaluation results, obtained for APs with different numbers of beams per sector, show that AoASteer typically selects a near-optimal LOS or NLOS link to establish communication, while significantly reducing the link establishment latency. For example, for 4-sector mm-wave antennas, AoASteer reduces the latency by 46 μs for 73% of our measurement cases compared to the IEEE 802.11ad beamsteering algorithm, while achieving the highest data rate of 6.7 Gbps for 92% of the cases.

Experimental evaluation of a novel fast beamsteering algorithm for link re-establishment in mm-wave indoor WLANs

The millimeter-wave (mm-wave) bands are currently being explored for multi-Gbps wireless local area networks (WLANs). Directional antennas are required to overcome the high attenuation inherent at the mm-wave frequencies. However, directionality makes link maintenance and establishment tasks complex, especially under node mobility, as slight misalignment of antenna beams between nodes leads to link disruption. Consequently, low latency beamsteering algorithms are needed for fast link re-establishment to support seamless data provisioning. Solutions based on exhaustive sequential scanning induce high latency, thereby disrupting communication. On the other hand, existing low latency proposals typically consider only static links, depend on additional hardware, or require a priori information about the network environment. In this paper, we propose a generic, fast mm-wave beamsteering algorithm that utilizes the previous valid link information to initiate the feasible antenna sector pair search and adaptively increases the sector search space around it to re-establish a link. Additionally, we experimentally evaluate the performance of our algorithm through measurements conducted in a real indoor environment using 60 GHz packet-radio transceivers. The results show that, compared to exhaustive sequential scanning, our algorithm reduces the required sector search space, and thereby the link re-establishment latency, by 89% on average compared to exhaustive sequential scanning.

ULLA-X: A programmatic middleware for generic cognitive radio network control

Classical cognitive radio concepts such as the cognitive radio manager or the cognitive engine aim to cleverly optimize radio configuration and networking setups to maximize performance throughout the network. However, these concepts regularly requires experimental verification and testing in complex, real-time and signal processing focused SDR frameworks, e.g. directly in FPGAs or in a software like GNURadio. This hinders rapid prototyping and deepens the gap between implementation-focused lower-layer and cross-layer/control plane oriented research. In this demo, we present a novel, fully-networked middleware for centrally controlling distributed radio link setups of SDRs and reconfigurable legacy devices. As an intermediate layer, ULLA-X allows technology-agnostic device parameter monitoring and reconfiguration based on an easy-to-learn programming language. Through its functionality, ULLA-X mitigates the complexities arising from using different radio platforms, and makes various configuration APIs readily accessible to standard research tools. In this demonstration, we showcase the capabilities of ULLA-X, and present by means of examples its adaptation capabilities for custom radio implementations and its programming concept for different SDR platforms.

mm-Wave Radar Based Gesture Recognition: Development and Evaluation of a Low-Power, Low-Complexity System

Gesture recognition is gaining attention as an attractive feature for the development of ubiquitous, context-aware, IoT applications. Use of radars as a primary or secondary system is tempting, as they can operate in darkness, high light intensity environments, and longer distances than many competitor systems. Starting from this observation, we present a generic, low-cost, mm-wave radar-based gesture recognition system. Among potential benefits of mm-wave radars are a high spatial resolution due to small wavelength, the availability of multiple antennas in a small area and the low interference due to the natural attenuation of mm-wave radiation. We experimentally evaluate our COTS solution considering eight different gestures and using two low-complexity classification algorithms: the unsupervised Self Organized Map (SOM) and the supervised Learning Vector Quantization (LVQ). To test robustness, we consider gestures performed by a human hand and a human body, at short and long distance. From our preliminary evaluations, we observe that LVQ and SOM correctly detect 75% and 60% of all gestures, respectively, from the raw, unprocessed data. The detection rate is significantly higher (>90%) for selected gesture groups. We argue that performance suffers due to inaccurate AoA estimation. Accordingly, we evaluate our system employing a two-radar setup that increases the estimation accuracy by 8-9%.

ULLA-X: a unified programmatic middleware for on-demand network reconfiguration

Software-defined networking provides new tools and offers more opportunities to actively manage large-scale network setups during runtime. However, controlling such installations in a unified and adaptive manner is inherently complex due to the remarkable diversity of management tools. In this paper we propose ULLA-X, a novel middleware that unifies network monitoring and on-demand reconfiguration through a universal network programming language. We introduce ULLA-X architecture and usage concept, which aims at bridging sophisticated network planning with practical network maintenance tools, resembling an “autonomic nervous system” for communication networks. Through its modular design, ULLA-X can be easily integrated into current network setups. While this paper is focused on introducing the ULLA-X concept and framework, we also present initial results from a prototype implementation for National Instruments wireless networked devices.

Environment-aware localization of femtocells for interference management

Femtocells are a promising approach to provide high data rates through autonomous configuration in indoor environments. However, due to the random and uncontrolled deployment of femtocells within users premises, interference between femtocells themselves and with macrocell base stations is a major issue. In this work, we look into the interference management problem and work towards the development of an interference mitigation algorithm based on the localization of randomly positioned femtocells using radio environmental information. In particular, we show that based on building floorplans and basic information on the urban landscape, femtocells can accurately localize themselves using macrocellular base stations as anchor nodes. Based on the localized femtocell positions, various channel allocation schemes are employed to mitigate interference.