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In this paper, we investigate a coordinated multipoint (CoMP) beamforming and power control problem for base stations (BSs) with a massive number of antenna arrays under coarse quantization at low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Unlike high-resolution ADC and DAC systems, non-negligible quantization noise that needs to be considered in CoMP design makes the problem more challenging. We first formulate total power minimization problems of both uplink (UL) and downlink (DL) systems subject to signal-to-interference-and-noise ratio (SINR) constraints. We then derive strong duality for the UL and DL problems under coarse quantization systems. Leveraging the duality, we propose a framework that is directed toward a twofold aim: to discover the optimal transmit powers in UL by developing iterative algorithm in a distributed manner and to obtain the optimal precoder in DL as a scaled instance of UL combiner. Under homogeneous transmit power and SINR constraints per cell, we further derive a deterministic solution for the UL CoMP problem by analyzing the lower bound of the SINR. Lastly, we extend the derived result to wideband orthogonal frequency-division multiplexing systems to optimize transmit power and beamformer for all subcarriers. Simulation results validate the theoretical results and proposed algorithms. [ABSTRACT FROM AUTHOR]

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty. [ABSTRACT FROM AUTHOR]

Employing low-resolution analog-to-digital converters (ADCs) for millimeter-wave receivers with large antenna arrays provides an opportunity to efficiently reduce power consumption of the receiver. Reducing ADC resolution, however, results in performance degradation due to non-negligible quantization error. In addition, the large number of radio frequency chains is still not desirable. Accordingly, conventional low-resolution ADC systems require more efficient designs to minimize the cost and complexity while maximizing performance. In this article, we discuss advanced low-resolution ADC receiver architectures that further improve the spectral and energy efficiency trade-off. To reduce both the numbers of RF chains and ADC bits, hybrid analog and digital beamforming is jointly considered with low-resolution ADCs. We explore the challenges in designing such receivers and present key insights on how the advanced architectures overcome such challenges. As alternative low-resolution ADC receivers, we also introduce receivers with learning-based detection. The receiver does not require explicit channel estimation, and thus is suitable for one-bit ADC systems. Finally, future challenges and research issues are discussed. [ABSTRACT FROM AUTHOR]

For low-resolution analog-to-digital converter (ADC) systems, only high-complexity receive antenna selection has been developed and transmit antenna selection has been limited to a single antenna selection in prior work. In this paper, we propose low-complexity receive antenna selection algorithms and analyze transmit antenna selection by considering antenna selection at a base station with large antenna arrays and low-resolution ADCs. For downlink antenna selection, we show a selection criterion with zero-forcing precoding equivalent to a perfect quantization system; sum rate increases with number of selected antennas; derivation of the sum rate loss function from using a antenna subset; and sum rate loss reaches a maximum at a point of total transmit power and decreases beyond that point to converge to zero. For wideband orthogonal-frequency-division-multiplexing (OFDM) systems, our results hold when entire subcarriers share a common subset of antennas. For uplink antenna selection, we generalize a greedy antenna selection criterion; propose a quantization-aware fast antenna selection algorithm using the criterion; and derive a lower bound on sum rate achieved by the proposed algorithm. For wideband OFDM systems, we extend our algorithm and derive a lower bound on its sum rate. Simulation results validate theoretical analyses and show increases in sum rate over conventional algorithms. [ABSTRACT FROM AUTHOR]

The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time. [ABSTRACT FROM AUTHOR]

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments. By leveraging the ability of $Q$ -learning to estimate future performance improvement rewards, we propose two algorithms: 1) closed loop power control (PC) for downlink voice over LTE (VoLTE) and 2) self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power so that the signal-to-interference plus noise ratio (SINR) of a user equipment (UE) meets the target SINR. It does so without the UE having to send power control requests. The SON fault management algorithm uses RL to improve the performance of an outdoor base station cluster by resolving faults in the network through configuration management. Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex performance optimization problem using RL. Simulation results show that our proposed RL-based algorithms outperform the industry standards today in realistic cellular communication environments. [ABSTRACT FROM AUTHOR]

In this paper, we investigate hybrid analog/digital beamforming for multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters for millimeter wave (mmWave) communications. In the receiver, we propose to split the analog combining subsystem into a channel gain aggregation stage followed by a spreading stage. Both stages use phase shifters. Our goal is to design the two-stage analog combiner to optimize mutual information (MI) between the transmitted and quantized signals by effectively managing quantization error. To this end, we formulate an unconstrained MI maximization problem without a constant modulus constraint on analog combiners, and derive a two-stage analog combining solution. The solution achieves the optimal scaling law with respect to the number of radio frequency chains and maximizes the MI for homogeneous singular values of a MIMO channel. We further develop a two-stage analog combining algorithm to implement the derived solution for mmWave channels. By decoupling channel gain aggregation and spreading functions from the derived solution, the proposed algorithm implements the two functions by using array response vectors and a discrete Fourier transform matrix under the constant modulus constraint on each matrix element. Therefore, the proposed algorithm provides a near-optimal solution for the unconstrained problem, whereas conventional hybrid approaches offer a near optimal solution only for a constrained problem. The closed-form approximation of the ergodic rate is derived for the algorithm, showing that a practical digital combiner with two-stage analog combining also achieves the optimal scaling law. Simulation results validate the algorithm performance and the derived ergodic rate. [ABSTRACT FROM AUTHOR]

We investigate uplink user scheduling for millimeter wave (mm-wave) hybrid analog/digital beamforming systems with low-resolution analog-to-digital converters (ADCs). Deriving new scheduling criteria for the mm-wave systems, we show that the channel structure in the beamspace, in addition to the channel magnitude and orthogonality, plays a key role in maximizing the achievable rates of scheduled users due to quantization error. The criteria show that to maximize the achievable rate for a given channel gain, the channels of the scheduled users need to have 1) as many propagation paths as possible with unique angle-of-arrivals (AoAs) and 2) even power distribution in the beamspace. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mm-wave zero-forcing receivers with low-resolution ADCs. We further propose a chordal distance-based scheduling algorithm that exploits only the AoA knowledge and analyze the performance by deriving ergodic rates in closed form. Based on the derived rates, we show that the beamspace channel leakage resulting from phase offsets between AoAs and quantized angles of analog combiners can lead to sum rate gain by reducing quantization error compared to the channel without leakage. The simulation results validate the sum rate performance of the proposed algorithms and the derived ergodic rate expressions. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the ergodic rate of single transmit antenna selection (TAS) in low-resolution analog-to-digital converter (ADC) systems. Using low-resolution ADCs is a potential power-reduction solution for multiple antenna systems. Low-resolution ADC systems with TAS can further reduce cost and power consumption in wireless transceivers. Considering such systems, we derive the approximated lower bound of the ergodic rate with TAS. Here, we exploit the approximated distribution of the sum of Weibull random variables to address the challenge involved in analyzing the quantization error. We, then, derive the approximated ergodic rate with TAS for a single receive antenna in closed form, which reveals the TAS gain in low-resolution ADC systems. The upper bound of a single transmit and single receive antenna system under coarse quantization is derived to compare with the ergodic rate of the TAS system. The analysis shows that the TAS method achieves a large improvement in ergodic rate with a moderate number of transmit antennas. Simulation results validate the derived ergodic rates and resulting intuition. [ABSTRACT FROM AUTHOR]

We propose a simple low-cost technique that enables civil global positioning system receivers and other civil global navigation satellite system (GNSS) receivers to reliably detect carry-off spoofing and jamming. The technique, which we call the power-distortion detector, classifies received signals as interference-free, multipath-afflicted, spoofed, or jammed according to observations of received power and correlation function distortion. It does not depend on external hardware or a network connection and can be readily implemented on many receivers via a firmware update. Crucially, the detector can with high probability distinguish low-power spoofing from ordinary multipath. In testing against more than 25 high-quality empirical datasets yielding more than 900,000 separate detection tests, the detector correctly alarms on all malicious spoofing or jamming attacks while maintaining a $<$0.6% single-channel false alarm rate. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a hybrid analog–digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that the optimal number of ADC bits is logarithmically proportional to the RF chain's signal-to-noise ratio raised to the $1/3$ power. Using the solutions, two proposed BA algorithms minimize the mean square quantization error of received analog signals under a total ADC power constraint. Contributions of this paper include 1) ADC bit-allocation algorithms to improve communication performance of a hybrid MIMO receiver, 2) approximation of the capacity with the BA algorithm as a function of channels, and 3) a worst-case analysis of the ergodic rate of the proposed MIMO receiver that quantifies system tradeoffs and serves as the lower bound. Simulation results demonstrate that the BA algorithms outperform a fixed-ADC approach in both spectral and energy efficiency, and validate the capacity and ergodic rate formula. For a power constraint equivalent to that of fixed 4-bit ADCs, the revised BA algorithm makes the quantization error negligible while achieving 22% better energy efficiency. Having negligible quantization error allows existing state-of-the-art digital beamformers to be readily applied to the proposed system. [ABSTRACT FROM PUBLISHER]

Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multiexposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions. Many HDR and MEF databases have a relatively small number of images and human opinion scores, obtained under stringently controlled conditions, thereby limiting realistic viewing. Moreover, many of these databases are intended to compare tone-mapping algorithms, rather than being specialized for developing and comparing image quality assessment models. To overcome these challenges, we conducted a massively crowdsourced online subjective study. The primary contributions described in this paper are: 1) the new ESPL-LIVE HDR Image Database that we created containing diverse images obtained by tone-mapping operators and MEF algorithms, with and without post-processing; 2) a large-scale subjective study that we conducted using a crowdsourced platform to gather more than 300 000 opinion scores on 1811 images from over 5000 unique observers; and 3) a detailed study of the correlation performance of the state-of-the-art no-reference image quality assessment algorithms against human opinion scores of these images. The database is available at http://signal.ece.utexas.edu/%7Edebarati/HDRDatabase.zip. [ABSTRACT FROM PUBLISHER]

A mostly digital pulsewidth-modulator-based delta-sigma $(\Delta\Sigma)$ analog-to-digital converter is proposed. This system takes advantage of the duration of pulses, rather than voltage or current, as the analog operand used in its closed-loop operation. Therefore, circuits that process the pulses are digital in nature and improve with scaling. Furthermore, the architecture allows inherently matched multibit quantizer/digital-to-analog converter (DAC) blocks by taking advantage of delay lines reusable in both quantization and DAC operation. A second novelty of this architecture is the modulator's adaptive excess delay, which synchronizes the asynchronous loop with an external reference clock. This mitigates the problems associated with nonuniform sampling. A first-order 3-b prototype of this architecture is presented. The core occupies an area of 0.0275 mm2 in a 0.18-\mu\text{m} CMOS process, consumes 2.7 mW, and measures a 45.1-dB SNR over a 2-MHz bandwidth. [ABSTRACT FROM PUBLISHER]

Non-Gaussian noise/interference severely limits communication performance of narrowband powerline communication (PLC) systems. Such noise/interference is dominated by periodic impulsive noise whose statistics varies with the AC cycle. The periodic impulsive noise statistics deviates significantly from that of additive white Gaussian noise, thereby causing dramatic performance degradation in conventional narrowband PLC systems. In this paper, we propose a robust transmission scheme and corresponding receiver methods to combat periodic impulsive noise in OFDM-based narrowband PLC. Towards that end, we propose (1) a time-frequency modulation diversity scheme at the transmitter and a diversity demodulator at the receiver to improve communication reliability without decreasing data rates; and (2) a semi-online algorithm that exploits the sparsity of the noise in the frequency domain to estimate the noise power spectrum for reliable decoding at the diversity demodulator. In the simulations, compared with a narrowband PLC system using Reed-Solomon and convolutional coding, whole-packet interleaving and DBPSK/BPSK modulation, our proposed transceiver methods achieve up to 8 dB gains in Eb/N0 with convolutional coding and a smaller-sized interleaver/deinterleaver. [ABSTRACT FROM PUBLISHER]

Asynchronous impulsive noise and periodic impulsive noises limit communication performance in OFDM powerline communication systems. Conventional OFDM receivers that assume additive white Gaussian noise experience degradation in communication performance in impulsive noise. Alternate designs assume a statistical noise model and use the model parameters in mitigating impulsive noise. These receivers require training overhead for parameter estimation, and degrade due to model and parameter mismatch. To mitigate asynchronous impulsive noise, we exploit its sparsity in the time domain, and apply sparse Bayesian learning methods to estimate and subtract the noise impulses. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones; (2) we add the information in the date tones to perform joint noise estimation and symbol detection; (3) we use decision feedback from the decoder to further enhance the accuracy of noise estimation. These algorithms are also embedded in a time-domain block interleaving OFDM system to mitigate periodic impulsive noise. Compared to conventional OFDM receivers, the proposed methods achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in asynchronous impulsive noise, and up to 6 dB in coded systems in periodic impulsive noise. [ABSTRACT FROM PUBLISHER]

Many wireless data communication systems such as LTE, and Wi-Fi, are increasingly facing interference that is much stronger than thermal noise. Interference may arise due to dense spatial reuse of spectrum intended to increase user data rates, or from other devices emitting radiation in the same spectrum, or from electronic circuitry within the communication platform. For a multi-antenna receiver operating in an interference-limited channel, we evaluate four diversity combining algorithms in terms of outage probability in the low-outage regime. The contributions of this paper are (1) derivation of closed-form expressions for the output signal-to-interference ratio (SIR) statistics of fixed weight, maximal ratio, selection and post-detection combining; (2) comparison of the relative outage performance of these algorithms; and (3) proposed diversity combining algorithms to reduce outage probability. Our results can be applied in analyzing the outage performance and throughput capacity of both centralized and decentralized interference-limited wireless networks. [ABSTRACT FROM PUBLISHER]

Characterizing interference statistics is central to the design and analysis of both physical layer and medium access control layer techniques to mitigate interference in a wireless network. The applicability of interference statistics, however, is limited by the assumptions adopted to derive the statistics in closed-form. Common assumptions for a decentralized wireless network include temporally independent user locations and an unbounded pathloss function. In this correspondence, we derive the joint temporal statistics of interference that capture the temporal correlation in the network along with the realistic assumption of a bounded pathloss function. The closed-form statistics are asymptotically exact for low tail probabilities, and match closely in simulations even when the tail probability is fairly high. The primary contributions are to i) show that joint interference statistics follow a multivariate Gaussian mixture distribution under the assumption of a bounded pathloss function, and ii) characterize the joint tail probability decay behavior for both bounded and unbounded pathloss functions. [ABSTRACT FROM PUBLISHER]