This paper considers mutual interference mitigation among automotive radars using frequency-modulated continuous wave (FMCW) signal and multiple-input multiple-output (MIMO) virtual arrays. For the first time, we derive a spatial-domain interference signal model that accounts for not only the time-frequency incoherence (e.g., different FMCW parameters and time offsets) but also the slow-time MIMO code incoherence and array configuration differences between the victim and interfering radars. Using the explicit interference signal model with the standard MIMO-FMCW object signal model, we turn the interference mitigation into a spatial-domain object detection under incoherent MIMO-FMCW interference. By exploiting the structural property of the derived interference model at both transmit and receive steering vector space, we derive a detector via beamforming optimization to achieve good detection performance and further propose an adaptive version of this detector to enhance its practical applicability. Performance evaluation using analytical closed-form expressions, synthetic-level simulation and system-level simulation confirms the effectiveness of our proposed detectors over selected baseline methods.

This paper considers mutual interference mitigation among automotive radars using frequency-modulated continuous wave (FMCW) signal and multiple-input multiple-output (MIMO) virtual arrays. For the first time, we derive a general interference signal model that fully accounts for not only the time-frequency incoherence, e.g., different FMCW configuration parameters and time offsets, but also the slow-time code MIMO incoherence and array configuration differences between the victim and interfering radars. Along with a standard MIMO-FMCW object signal model, we turn the interference mitigation into a spatial-domain object detection under incoherent MIMO-FMCW interference described by the explicit interference signal model, and propose a constant false alarm rate (CFAR) detector. More specifically, the proposed detector exploits the structural property of the derived interference model at both \emph{transmit} and \emph{receive} steering vector space. We also derive analytical closed-form expressions for probabilities of detection and false alarm. Performance evaluation using both synthetic-level and phased array system-level simulation confirms the effectiveness of our proposed detector over selected baseline methods.

We analyze low-power short-range wireless communications through a low-rank fading channel - a bonafide use case in many communication scenarios requiring simple wireless connectivity with much relaxed constraints on throughput and data latency. This is certainly true, for instance, in low-complexity wireless channels in the low-rate wireless personal area networks (LR-WPANs). Low-rate communication on control channels in wireless networks is another relevant example. Specifically, we characterize the capacity of a low-rank wireless channel with varying fading severity at low signal-to-noise ratios (SNRs). The rank deficiency is incorporated by introducing pinhole condition in the channel. The channel capacity degradation with fading severity at high SNRs is well known: the probability of deep fades increases significantly with higher fading severity resulting in poor performance. Our analysis of the double-fading pinhole channel at low-SNR shows a very counter-intuitive result that - \emph{higher fading severity enables higher capacity at sufficiently low SNR}. The underlying reason is that at low SNRs, ergodic capacity depends crucially on the probability distribution of channel peaks (simply tail distribution); for the pinhole channel, the tail distribution improves with increased fading severity. This allows a transmitter operating at low SNR to exploit channel peaks `more efficiently' resulting in net improvement in achievable spectral efficiency. We derive a new key result quantifying the above dependence for the double-Nakagami-$m$ fading pinhole channel - that is, the ergodic capacity ${C} \propto (m_T m_R)^{-1}$ at low SNR, where $m_T m_R$ is the product of fading (severity) parameters of the two independent Nakagami-$m$ fadings involved.

We characterize the capacity of a low-rank wireless channel with varying fading severity at low signal-to-noise ratios (SNRs). The channel rank deficiency is achieved by incorporating pinhole condition. The capacity degradation with fading severity at high SNRs is well known: the probability of deep fades increases significantly with higher fading severity resulting in poor performance. Our analysis of the dyadic pinhole channel at low-SNR shows a very counter-intuitive result that - \emph{higher fading severity enables higher capacity at sufficiently low SNR}. The underlying reason is that at low SNRs, ergodic capacity depends crucially on the probability distribution of channel peaks (tail distribution); for the pinhole channel, the tail distribution improves with fading severity. This allows a transmitter operating at low SNR to exploit channel peaks `more efficiently' and hence improves spectral efficiency. We derive a new key result quantifying the above dependence for the double-Nakagami-$m$ fading pinhole channel - the capacity ${C} \propto (m_T m_R)^{-1}$ at low SNR, where $m_T m_R$ is the severity parameters (product) of the fadings involved.

With the introduction of Orthogonal Frequency Division Multiple Access (OFDMA) in 802.11ax, the role of the Access Point (AP) in Wi-Fi networks changes significantly, thanks to the opportunity of implementing more complex scheduling logic to handle Downlink (DL) traffic flows and simultaneously act as coordinator of Multi User (MU) Uplink (UL) transmissions. In this context, it becomes necessary to develop reliable network analysis and simulation tools that allow for an in-depth investigation of the trade-offs involved in the usage of OFDMA, especially considering that the standard leaves much of the actual scheduling algorithmic details to vendor-specific implementation. In this work we present a series of results highlighting how several network settings have an impact on throughput and Head-of-Line Delay, in a network that employs multiple 802.11ax features such as OFDMA and the MU Enhanced Distributed Channel Access (EDCA) Parameter Set, while also containing legacy devices. The results are obtained via both the newly re-designed ns-3 wifi module and an original analytical framework, based on the well-established Bianchi 802.11 model.

Advanced driver-assistance system features in current vehicles (as precursor to fully autonomous, connected vehicular systems) will rely on 5G network links. Specifically, enabling various real-time operational aspects (e.g. emergency messaging), performance of ultra-reliable low-latency communications (URLLC) specified by 3GPP Release 16 is of direct relevance. A key challenge in achieving URLLC goals is the natural tension between simultaneously achieving high reliability (low block error rate or BLER) and low latency. When operating in a dynamic (i.e. significant time-varying channels due to vehicular mobility) environment, link adaptation is fundamental to URLLC implementation. In this paper, we introduce a new Markov Chain model for link adaptation that predicts the three key performance indicators (KPIs): end-to-end link latency, throughput and BLER as a function of the modulation and coding scheme used. The predictions from the Markov Chain model are supported by Monte Carlo simulations for various mobility scenarios, to explore how 5G network parameters may be adapted to achieve desired benchmarks, e.g. maximizing link throughput while providing strict latency and BLER bounds.

IEEE 802.11ax partitions a regular 20MHz channel into smaller sub-channels called resource units to support simultaneous multiuser operation using orthogonal frequency division multiple access (OFDMA). Uplink OFDMA random access (UORA) in IEEE 802.11ax allows stations to transmit via a scheduled random access mechanism. UORA is initiated via a trigger frame which aside from serving as a synchronization mechanism, also informs stations which resource units are allowed for random access. Using the trigger frame information, the stations engage in an OFDMA backoff process to win access to a resource unit. Similar to slotted ALOHA, the maximum normalized throughput of UORA is only 37 percent due to high probability of collisions at high loads. To reduce collisions, we equip UORA with carrier sensing capability resulting in a new uplink hybrid UORA (H-UORA) OFDMA access mechanism. Unlike other multi-carrier CSMA methods previously proposed in literature, H-UORA is an easily implementable modification to current 802.11ax WLANs. We show that H-UORA can achieve a normalized throughput of at least 80 percent (which increases further depending on the buffering capabilities of the access point) using various numerical analysis and simulations.

In this letter, we investigate Mission Critical Push-to-Talk (MCPTT) communications used by emergency first responders that are currently migrating from legacy trunked digital Land Mobile Radio (LMR) systems to Fourth Generation (4G) Long Term Evolution (LTE) networks. Following the Key Performance Indicators (KPIs) proposed by the Third Generation Partnership Project (3GPP) for performance evaluation of public safety networks. We provide a comparative study for the two networks, and highlight the trade-offs as a function of the respective system parameters.

Small-cell LTE and Wi-Fi networks are both currently deployed in the unlicensed 5 GHz bands globally, leading to the need for new coexistence regulations between two very different access technologies. 3GPP standardized LTE Licensed Assisted Access (LTE-LAA) addresses the above coexistence challenge with Wi-Fi through incorporation of similar sensing and back-off features. The success of LAA's fair and efficient coexistence with Wi-Fi can be considered a benchmark for collaborative cellular operation in unlicensed bands.

Packet-level network simulators such as ns-3 require accurate physical (PHY) layer models for packet error rate (PER) for wideband transmission over fading wireless channels. To manage complexity and achieve practical runtimes, suitable link-to-system mappings can convert high fidelity PHY layer models for use by packet-level simulators. This work reports on two new contributions to the ns-3 Wi-Fi module, which presently only contains error models for Single Input Single Output (SISO), additive white Gaussian noise (AWGN) channels. To improve this, a complete implementation of a link-to-system mapping technique for IEEE 802.11 TGn fading channels is presented that involves a method for efficient generation of channel realizations within ns-3. The runtimes for the prior method suffers from scalability issues with increasing dimensionality of Multiple Input Multiple Output (MIMO) systems. We next propose a novel method to directly characterize the probability distribution of the"effective SNR" in link-to-system mapping. This approach is shown to require modest storage and not only reduces ns-3 runtime, it is also insensitive to growth of MIMO dimensionality. We describe the principles of this new method and provide details about its implementation, performance, and validation in ns-3.

An important challenge for ns-3 is to enable efficient performance evaluation of increasingly dense and heterogeneous networks, cognizant of cross-layer (specifically, Layers 1 & 2) interactions. In this work (a continuation of U. Washington efforts), we present improved physical layer abstractions for a key component underlying all 802.11 WLAN MAC performance evaluation - the Clear Channel Assessment (CCA) procedure central to CSMA/CA - for implementation in the ns-3 simulator. We model the preamble correlation process as typically implemented in 802.11 radio and represent the resulting probability of detection as a look-up table with a parameterized correlation threshold for different receive sensitivity strategies. Further, we also added a new carrier sense threshold adjustment mechanism to allow nodes to enable bypassing the default (and to date, fixed) -82dBm threshold. Such a capability aligns ns-3 for performance evaluation of dense networks equipped with new spatial reuse mechanisms. We demonstrate this via simulation of spatial reuse gains from dynamic sensitivity control (DSC) that are verified against IEEE 802.11ax standards group contributions. Using simulation results from a fixed rate multi-BSS network, we then identify valuable design guidelines to maximize the aggregate throughput with DSC.

We introduce a new pipeline for analyzing and mitigating radio frequency interference (RFI), which we call Sky-Subtracted Incoherent Noise Spectra (SSINS). SSINS is designed to identify and remove faint RFI below the single baseline thermal noise by employing a frequency-matched detection algorithm on baseline-averaged amplitudes of time-differenced visibilities. We demonstrate the capabilities of SSINS using the Murchison Widefield Array (MWA) in Western Australia. We successfully image aircraft flying over the array via digital television (DTV) reflection detected using SSINS and summarize an RFI occupancy survey of MWA Epoch of Reionization data. We describe how to use SSINS with new data using a documented, publicly available implementation with comprehensive usage tutorials.

The electric vehicles have become the need of the hour and have seen a reasonably good market growth in the past few years because they are clean, quiet and easy to operate. The electric motor driven system needs to be controlled through a mechanism for improving the performance in a prearranged manner to ensure the correct speed output when required. The energy optimal speed control is a challenge on the efficiency ground for electric vehicle and to boost power stability the dual motor concept is introduced in this paper. The asynchronous motors are controlled using the fuzzy logic controller in a closed loop mechanism taking instantaneous rotor speed as input. The electric vehicle model is designed in the MATLAB/Simulink environment and significant improvements in the results are depicted after the analysis.

Results are presented from an extensive campaign of link simulations for multi-user multi-input multi-output (MU-MIMO) scenarios of 802.11ac wireless local area networks (WLAN) for use within a link-to-system mapping framework for ns-3 network simulation. As in [2], Exponential Effective SNR Mapping (EESM) is used inclusive of the impact of channel estimation, but this works extends beyond SISO to MU-MIMO. MATLAB® link simulation results using the WLAN Toolbox™ are used to generate an error rate table lookup for EESM to produce a corresponding packet error rate (PER) for use by ns-3. The simulation programs are made available to allow reproduction and extending of the baseline results.

I. BACKGROUND When multiple packets arrive at a server, packets wait in queue according to some discipline typically first-come, first served (FCFS) to process the arrivals. Queue operations are denoted by Kendall’s notation that captures three factors: a) the input or arrival process, b) the server or departure process and c) the # of servers. The most canonical queue studied is the M/M/1/C queue, where: ”M” indicates a Markovian arrival process, i.e. a Poisson process whereby packet inter-arrival times are exponentially distributed with mean 1 λ (or equiv. the arrival rate in packets/sec is λ). The second ”M” indicates packet service time (the duration from beginning to end of service), that is also distributed exponentially with mean service time μ; the service time is indep. of the inter-arrival process. The ”1” indicates that there is only one server and ”C” denotes the buffer capacity (i.e. a maximum of C packets may wait in queue and any further arrivals are denied entry/lost). An M/M/1/C queue abstraction is shown in Figure 1.

Sharing of hitherto licensed spectrum will be an increasing reality for future (next-gen) wireless broadband networks. In prior work, the authors introduced a novel method to quantify the opportunity cost of spectrum sharing between a single licensee (primary) - unlicensed (secondary) user pair that allowed meaningful comparison of various market mechanisms at equilibrium when applied to sharing. In this submission, we explore an important extension: how the opportunity costs and market equilibrium (prices and per-user rate) is influenced as a function of system scaling, i.e., N,M primary and secondary network users, respectively, under the assumption of `symmetric' physical layer for each user.

Compute-and-forward (C&F) recently finds new applications in random-access networks focusing on the single access point (AP) scenario. In this paper, we extend the use of C&F from the single AP scenario to the multi-AP scenario. To achieve this, we identify two major challenges and propose two novel solutions. First, we introduce an AP cooperation problem and develop an efficient distributed algorithm. Second, we introduce a joint channel estimation and active user recovery problem and propose a solution based on spare recovery techniques. In addition, we provide accurate throughput and delay expressions for C&F-based carrier-sense multiple access (CSMA) protocols. These expressions, together with our trace-driven simulations, demonstrate the significant advantages of C&F-based CSMA over conventional CSMA in the multi-AP scenario.

This paper describes work to create an unmanned aerial system (UAS) testbed, built on commercial off-the-shelf hardware and open source software components, as a platform for networking and spectrum related research. Of particular interest is characterization of (low altitude) air-ground wireless links between an unmanned aerial vehicle (UAV) and a ground node, for which little prior data is available. UAVs are mounted with software defined radios (SDR) capable of transmitting IEEE 802.11 packets to a ground node. Multiple static tests are executed to collect data in different scenarios characterizing the dependence of link quality on wireless parameters and physical parameters such as aircraft altitude and distance. A major contribution of this work is the creation of a public database that will enable new propagation models using this data, and in turn will drive more accurate UAV network simulations.

Regional language extraction from a natural scene image is always a challenging proposition due to its dependence on the text information extracted from Image. Text Extraction on the other hand varies on different lighting condition, arbitrary orientation, inadequate text information, heavy background influence over text and change of text appearance. This paper presents a novel unified method for tackling the above challenges. The proposed work uses an image correction and segmentation technique on the existing Text Detection Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses standard PVAnet architecture to select features and non maximal suppression to detect text from image. Text recognition is done using combined architecture of MaxOut convolution neural network (CNN) and Bidirectional long short term memory (LSTM) network. After recognizing text using the Deep Learning based approach, the native Languages are translated to English and tokenized using standard Text Tokenizers. The tokens that very likely represent a location is used to find the Global Positioning System (GPS) coordinates of the location and subsequently the regional languages spoken in that location is extracted. The proposed method is tested on a self generated dataset collected from Government of India dataset and experimented on Standard Dataset to evaluate the performance of the proposed technique. Comparative study with a few state-of-the-art methods on text detection, recognition and extraction of regional language from images shows that the proposed method outperforms the existing methods.