arXiv subjects

Danijela Cabric

Publications and source records attributed to Danijela Cabric.

At least 73 records · Page 4Linked to original sources

arXiv2019

Unlicensed Spectrum Sharing for Massive Internet-of-Things Communications

The unlicensed spectrum, although free, has become an invaluable resource toward enabling massive Internet-of-things (IoT) applications, where Internet-enabled devices are deployed at a large scale. However, realizing massive IoT connectivity over unlicensed bands requires efficient spectrum sharing among IoT devices and fair coexistence with other wireless networks. In this article, we discuss several spectrum sharing methods to address these intra- and inter-network sharing issues. To this end, we first consider ALOHA-based networks that avoid spectrum sensing, yet rely on diversity to improve connection density of random access. Then, we present sensing-based solutions such as unlicensed cellular access that help support a wider range of applications with different rate requirements and connection densities. Finally, we highlight future research directions for massive IoT connectivity over the unlicensed spectrum.

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arXiv2019

Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Initial access (IA) is a fundamental physical layer procedure in cellular systems where user equipment (UE) detects nearby base station (BS) as well as acquire synchronization. Due to the necessity of using antenna array in millimeter-wave (mmW) IA, the channel spatial information can also be inferred. The state-of-the-art directional IA (DIA) uses sector sounding beams with limited angular resolution, and thus requires additional dedicated radio resources, access latency and overhead for refined beam training. To remedy the problem of access latency and overhead in DIA, this work proposes to use a quasi-omni pseudorandom sounding beam for IA, and develops a novel algorithm for joint initial access and fine resolution initial beam training without requiring extra radio resources. We provide the analysis of the proposed algorithm miss detection rate under synchronization error, and further derive Cramér-Rao lower bound of angular estimation under frequency offset. Using QuaDRiGa simulator with mmMAGIC model at 28 GHz, the numerical results show that the proposed approach is advantageous to DIA with hierarchical beam training. The proposed algorithm offers up to two order of magnitude access latency saving compared to DIA, when the same discovery, post training SNR, and overhead performance are targeted. This conclusion holds true in various propagation environments and 3D locations of a mmW pico-cell with up to 140m radius.

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arXiv2018

Distributed Wideband Sensing-based Architecture for Unlicensed Massive IoT Communications

Providing Internet connectivity to a massive number of Internet-of-things (IoT) objects over the unlicensed spectrum requires: (i) identifying a very large number of narrowband channels in a wideband spectrum and (ii) aggressively reusing the available channels over space to accommodate the high density of IoT devices. To this end, we propose a sensing-based architecture that identifies spectral and spatial resources at a fine resolution. In particular, we first propose a sensing assignment scheduler, where each base station (BS) is assigned a subset of the spectrum to sense at a high resolution. We then propose a distributed sensing algorithm, where BSs locally process and share their sensing reports, so that each BS obtains occupancy information of the wideband spectrum at its location. Once the spatio-spectral resource blocks are identified, we further propose a distributed resource allocation algorithm that maintains high spatial reuse of spectral opportunities while limiting the intra-network and inter-network interference. Numerical simulations are presented to validate the effectiveness of the proposed distributed algorithms, comparing them to centralized and non-cooperative schemes. It is shown that our architecture identifies more spatio-spectral resources, with lower misdetection of incumbents. As a result, more IoT devices are connected with limited interference into incumbents.

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arXiv2018

Performance Analysis of Uplink Cellular IoT Using Different Deployments of Data Aggregators

Data aggregation is an effective solution to enable cellular support of Internet-of-things (IoT) communications. Indeed, it helps alleviate channel congestion, reduce the communication range, and extend battery-lifetime. In this paper, we use stochastic geometry to analyze the performance of uplink cellular IoT using different deployment strategies of aggregators, including terrestrial and aerial ones, e.g., drones or unmanned aerial vehicles. We focus on IoT-specific performance metrics, that are typically used by 3GPP. Specifically, we derive closed-form expressions of the average transmit power consumption, which is key to determine the lifetime of IoT devices, as well as the maximum coupling loss, which is essential to determine the maximum coverage the cellular system can support. Simulation results are presented to validate the derived theoretical expressions. It is shown that aerial aggregators can significantly extend the device lifetime and provide superior coverage compared to other deployment strategies. In addition, random deployment performs well when aggregators are densely deployed, whereas optimizing the location of a single terrestrial aggregator is beneficial when devices are more clustered.

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arXiv2018

Distributed Wideband Spatio-Spectral Sensing for Unlicensed Massive IoT Communications

In this paper, we propose a dynamic spectrum sensing-based architecture to provide connectivity for a massive number of Internet-of-things (IoT) objects over the unlicensed spectrum. Specifically, the architecture relies on deploying sensing access points (SAPs), e.g., small cells with sensing capabilities, that aim to (i) identify a large number of narrowband channels in a wideband spectrum, as many massive IoT applications have low-rate requirements, and (ii) aggressively reuse the unlicensed channels at the SAPs' locations as IoT devices typically transmit at low power, occupying a small spatial footprint. Instead of enforcing each SAP to sense the entire spectrum, we develop a sensing assignment scheduler that ensures each one senses a subset of the spectrum. We then develop a distributed spatio-spectral cooperative sensing algorithm that enables each SAP to have local information about the occupancy of the entire spectrum. We present numerical simulations to validate the effectiveness of the proposed system in the presence of WiFi access points (APs). It is shown that the proposed system outperforms non-cooperative and centralized schemes in terms of reliably identifying more available spatio-spectral blocks with a lower misdetection of transmitting WiFi APs.

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arXiv2018

Tracking Sparse mmWave Channel under Time Varying Multipath Scatterers

Due to severe signal attenuation at millimeter-wave (mmWave) frequencies large antenna arrays are required at both base station and user equipment to achieve necessary beamfoming gain and compensate for the signal power loss. The initial access and beamforming algorithms are typically designed assuming sparsity of mmWave channels, resulting from a very few significant multipath clusters, and considering fixed locations of terminals and scatterers. Channel tracking algorithms have been proposed to account for channel variations due to user mobility. Existing works did not consider mobility of the scatterers, which adds new challenges and opportunities into a channel tracking problem. In this work, we consider a more realistic assumption of mobile scatterers and their impact on channel tracking algorithms. We propose a novel channel tracking algorithm that takes into account the dynamics of cluster evolution, and adaptively tracks channel parameters with the objective to reduce training overhead. We also propose a simple implementation of aperiodic tracking to accommodate tracking to different channel variations. We analyze the performance of the proposed tracking algorithm under highly dynamic channels, and compare it to existing channel tracking algorithms with respect to tracking accuracy, achievable rate, and required training overhead, when aperiodic and periodic trackings are used.

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arXiv2018

Spectrum Sharing Protocols based on Ultra-Narrowband Communications for Unlicensed Massive IoT

Ultra-narrowband (UNB) communications is an emerging paradigm that tackles two challenges to realizing massive Internet-of-things (IoT) connectivity over the unlicensed spectrum: the intra-network sharing, i.e., how the spectrum is shared among IoT devices, and the inter-network sharing, i.e., the coexistence of the IoT network with other incumbent networks. Specifically, intra-network sharing is enabled by using extremely narrowband signals to connect a massive number of IoT devices without any prior network synchronization. Further, to enhance robustness to incumbent interference, each IoT packet is sent multiple times, each at a different frequency within a single band. Nevertheless, the interplay between intra-network sharing and inter-technology coexistence at a large scale remains unclear. Thus, in this paper, we develop an analytical framework to model and analyze UNB networks. We use stochastic geometry to derive the probability of successful transmission, identifying the impact of intra- and inter-network interference on the performance. In addition to analyzing the existing single-band access protocols, we present two multiband schemes, where each BS listens to a single band for practical implementation. Different access protocols are further compared in terms of the transmission capacity, i.e., the maximum number of IoT devices a UNB protocol can support in the presence of incumbent networks. Several design insights are gleaned from the derived closed-form expressions, and simulation results are further provided to validate them.

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arXiv2018

Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity

The imperfections in the RF frontend of different transmitters can be used to distinguish them. This process is called transmitter identification using RF fingerprints. The nonlinearity in the power amplifier of the RF frontend is a significant cause of the discrepancy in RF fingerprints, which enables transmitter identification. In this work, we use deep learning to identify different transmitters using their nonlinear characteristics. By developing a nonlinear model generator based on extensive measurements, we were able to extend the evaluation of transmitter identification to include a larger number of transmitters beyond what exists in the literature. We were also able to study the impact of transmitter variability on identification accuracy. Additionally, many other factors were considered including modulation type, length of data used for identification, and type of data being transmitted whether identical or random under a realistic channel model. Simulation results were compared with experiments which confirmed similar trends.

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arXiv2018

Distributed UAV Placement Optimization for Cooperative Line-of-Sight MIMO Communications

Cooperative communication using unmanned aerial vehicles (UAVs) is a promising technology for infrastructureless wireless networks. One of the key challenges in UAV based communications is the backhaul throughput. In this paper, we propose optimization of the UAV swarm positions to achieve a high mulitplexing gain in line-of-sight (LoS) MIMO backhaul. We develop two distributed algorithms to position the UAVs such that each UAV moves a minimal distance to realize the highest capacity LoS MIMO channel. The first approach uses iterative gradient descent (GD) and the second uses iterative brute force (BF). Simulations show that both algorithms can achieve up to 6 times higher capacity compared to the approach relying on random UAV placement, earlier proposed in the literature. BF has the advantage of not requiring any location information, while GD is less sensitive to errors in motion.

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arXiv2018

Software Defined Radio Implementation of Carrier and Timing Synchronization for Distributed Arrays

The communication range of wireless networks can be greatly improved by using distributed beamforming from a set of independent radio nodes. One of the key challenges in establishing a beamformed communication link from separate radios is achieving carrier frequency and sample timing synchronization. This paper describes an implementation that addresses both carrier frequency and sample timing synchronization simultaneously using RF signaling between designated master and slave nodes. By using a pilot signal transmitted by the master node, each slave estimates and tracks the frequency and timing offset and digitally compensates for them. A real-time implementation of the proposed system was developed in GNU Radio and tested with Ettus USRP N210 software defined radios. The measurements show that the distributed array can reach a residual frequency error of 5 Hz and a residual timing offset of 1/16 the sample duration for 70 percent of the time. This performance enables distributed beamforming for range extension applications.

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arXiv2018

Performance, Power, and Area Design Trade-offs in Millimeter-Wave Transmitter Beamforming Architectures

Millimeter wave (mmW) communications is the key enabler of 5G cellular networks due to vast spectrum availability that could boost peak rate and capacity. Due to increased propagation loss in mmW band, transceivers with massive antenna array are required to meet link budget, but their power consumption and cost become limiting factors for commercial systems. Radio designs based on hybrid digital and analog array architectures and the usage of radio frequency (RF) signal processing via phase shifters have emerged as potential solutions to improve energy efficiency and deliver performances close to digital arrays. In this paper, we provide an overview of the state-of-the-art mmW massive antenna array designs and comparison among three architectures, namely digital array, sub-array, and fully-connected hybrid array. The comparison of performance, power, and area for these three architectures is performed for three representative 5G use cases, which cover a range of pre-beamforming SNR and multiplexing regimes. This is the first study to comprehensively model and analyze all design aspects and criteria including: 1) optimal linear precoder, 2) impact of quantization error in DAC and phase shifters, 3) RF signal distribution network, 4) power and area estimation based on state-of-the-art mmW circuits including baseband precoding, digital signal distribution, DACs, oscillators and mixers, phase shifters, RF signal distribution, and power amplifiers. The results show that the digital array is the most power and area efficient compared against optimal design for each architecture. Our analysis shows digital array benefits greatly from multi-user multiplexing. The analysis also reveals that sub-array is limited by reduced beamforming gain due to array partitioning, and system bottleneck of the fully-connected hybrid architecture is the excessively power hungry RF signal distribution network.

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arXiv2018

Tracking Sparse mmWave Channel: Performance Analysis under Intra-Cluster Angular Spread

Millimeter-wave (mmWave) systems require a large number of antennas at both base station and user equipment for a desirable link budget. Due to time varying channel under user mobility, up-to-date channel state information (CSI) is critical to obtain the required beamforming gain.. The mmWave sparse multipath channel is commonly exploited in designing tracking algorithms but practical angular spread is often overlooked. In this work, we study the performance bound of tracking accuracy in sparse mmWave channel that includes intra-cluster angular spreads. Power gain from angle-steering-based beamforming using tracked CSI is then analyzed. The theoretical study provides a design guideline beam-width in angle-steering under different intra-cluster angular spreads. We verify the results with two common tracking algorithms including sector beam tracking and maximum likelihood channel tracking.

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arXiv2017

Learning Wireless Networks' Topologies Using Asymmetric Granger Causality

Sharing spectrum with a communicating incumbent user (IU) network requires avoiding interference to IU receivers. But since receivers are passive when in the receive mode and cannot be detected, the network topology can be used to predict the potential receivers of a currently active transmitter. For this purpose, this paper proposes a method to detect the directed links between IUs of time multiplexing communication networks from their transmission start and end times. It models the response mechanism of commonly used communication protocols using Granger causality: the probability of an IU starting a transmission after another IU's transmission ends increases if the former is a receiver of the latter. This paper proposes a non-parametric test statistic for detecting such behavior. To help differentiate between a response and the opportunistic access of available spectrum, the same test statistic is used to estimate the response time of each link. The causal structure of the response is studied through a discrete time Markov chain that abstracts the IUs' medium access protocol and focuses on the response time and response probability of 2 IUs. Through NS-3 simulations, it is shown that the proposed algorithm outperforms existing methods in accurately learning the topologies of infrastructure-based networks and that it can infer the directed data flow in ad hoc networks with finer time resolution than an existing method.

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arXiv2017

Channel Access Method Classification For Cognitive Radio Applications

Motivated by improved detection and prediction of temporal holes, we propose a two stage algorithm to classify the channel access method used by a primary network. The first stage extends an existing fourth-order cumulant-based modulation classifier to distinguish between TDMA, OFDMA, and CDMA. The second stage proposes a novel collision detector using the sample variance of the same cumulant to detect contention-based channel access methods. Our proposed method is blind and independent of the received SNR. Simulations show that our classification of TDMA, OFDMA, and CDMA is robust to network load while detection of contention outperforms existing methods.

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arXiv2015

Compressive Detection of Random Subspace Signals

The problem of compressive detection of random subspace signals is studied. We consider signals modeled as $\mathbf{s} = \mathbf{H} \mathbf{x}$ where $\mathbf{H}$ is an $N \times K$ matrix with $K \le N$ and $\mathbf{x} \sim \mathcal{N}(\mathbf{0}_{K,1},σ_x^2 \mathbf{I}_K)$. We say that signal $\mathbf{s}$ lies in or leans toward a subspace if the largest eigenvalue of $\mathbf{H} \mathbf{H}^T$ is strictly greater than its smallest eigenvalue. We first design a measurement matrix $\mathbfΦ=[\mathbfΦ_s^T,\mathbfΦ_o^T]^T$ comprising of two sub-matrices $\mathbfΦ_s$ and $\mathbfΦ_o$ where $\mathbfΦ_s$ projects the signals to the strongest left-singular vectors, i.e., the left-singular vectors corresponding to the largest singular values, of subspace matrix $\mathbf{H}$ and $\mathbfΦ_o$ projects it to the weakest left-singular vectors. We then propose two detectors which work based on the difference in energies of the samples measured by two sub-matrices $\mathbfΦ_s$ and $\mathbfΦ_o$ and prove their optimality. Simplified versions of the proposed detectors for the case when the variance of noise is known are also provided. Furthermore, we study the performance of the detector when measurements are imprecise and show how imprecision can be compensated by employing more measurement devices. The problem is then re-formulated for the case when the signal lies in the union of a finite number of linear subspaces instead of a single linear subspace. Finally, we study the performance of the proposed methods by simulation examples.

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arXiv2015

Compressive Identification of Active OFDM Subcarriers in Presence of Timing Offset

In this paper we study the problem of identifying active subcarriers in an OFDM signal from compressive measurements sampled at sub-Nyquist rate. The problem is of importance in Cognitive Radio systems when secondary users (SUs) are looking for available spectrum opportunities to communicate over them while sensing at Nyquist rate sampling can be costly or even impractical in case of very wide bandwidth. We first study the effect of timing offset and derive the necessary and sufficient conditions for signal recovery in the oracle-assisted case when the true active sub-carriers are assumed known. Then we propose an Orthogonal Matching Pursuit (OMP)-based joint sparse recovery method for identifying active subcarriers when the timing offset is known. Finally we extend the problem to the case of unknown timing offset and develop a joint dictionary learning and sparse approximation algorithm, where in the dictionary learning phase the timing offset is estimated and in the sparse approximation phase active subcarriers are identified. The obtained results demonstrate that active subcarrier identification can be carried out reliably, by using the developed framework.

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arXiv2015

Robust Cooperative Spectrum Sensing Scheduling Optimization in Multi-Channel Dynamic Spectrum Access Networks

Dynamic spectrum access (DSA) enables secondary networks to find and efficiently exploit spectrum opportunities. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, and thus it increases transmission throughput. However, in a multi-channel system, the problem becomes more complex since the benefits of assigning users to sense channels in parallel must also be considered. A sensing schedule, indicating to each user the channel that it should sense at different sensing moments, must be thus created to optimize system performance. In this paper, we formulate the general sensing scheduling optimization problem and then propose several sensing strategies to schedule the users according to network parameters with homogeneous sensors. Later on we extend the results to heterogeneous sensors and propose a robust scheduling design when we have traffic and channel uncertainty. We propose three sensing strategies, and, within each one of them, several solutions, striking a balance between throughput performance and computational complexity, are proposed. In addition, we show that a sequential channel sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, a parallel channel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel channel sensing strategy achieves the best performance in all scenarios at the cost of extra memory and computation complexity.

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arXiv2015

Improved Eigenvalue-based Spectrum Sensing via Sensor Signal Overlapping

Eigenvalue-based detectors are considered as an important method of spectrum sensing since they do not require the information about the primary user (PU) signal. In this paper we propose a method to improve the performance of the eigenvalue-based detector. The proposed method introduces a new test statistic based on combinatorial matrix with components which are overlapping subgroups extracted from the array of received signals. As a result, its covariance matrix has a larger maximum eigenvalue and trace value than the one without overlapping. Simulation results show that our proposed method can further improve the detection performance of the optimal eigenvalue-based detector. The paper also shows the effect of different overlapping methods on the receiver operating characteristic curve.

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