Showing papers on "Communication channel published in 2014"
TL;DR: An adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel is developed and a new hybrid analog/digital precoding algorithm is proposed that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions.
Abstract: Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWave-specific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel. To enable the efficient operation of this algorithm, a novel hierarchical multi-resolution codebook is designed to construct training beamforming vectors with different beamwidths. For single-path channels, an upper bound on the estimation error probability using the proposed algorithm is derived, and some insights into the efficient allocation of the training power among the adaptive stages of the algorithm are obtained. The adaptive channel estimation algorithm is then extended to the multi-path case relying on the sparse nature of the channel. Using the estimated channel, this paper proposes a new hybrid analog/digital precoding algorithm that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions. Simulation results show that the proposed low-complexity channel estimation algorithm achieves comparable precoding gains compared to exhaustive channel training algorithms. The results illustrate that the proposed channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference.
2,424 citations
TL;DR: The flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks, and its main characteristics are analyzed.
Abstract: Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; and control applications require very short response times. We envision a unified physical layer waveform, referred to as generalized frequency division multiplexing (GFDM), to address these requirements. In this paper, we analyze the main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: 1) the means for engineering the waveform's spectral properties; 2) analytical analysis of symbol error performance over different channel models; 3) concepts for MIMO-GFDM to achieve diversity; 4) preamble-based synchronization that preserves the excellent spectral properties of the waveform; 5) bit error rate performance for channel coded GFDM transmission using iterative receivers; 6) relevant application scenarios and suitable GFDM parameterizations; and 7) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.
809 citations
07 Sep 2014
TL;DR: This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops) in a low-cost system that can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles.
Abstract: Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.
761 citations
TL;DR: This paper extends the popular Wireless World Initiative for New Radio (WINNER) channel model with new features to make it as realistic as possible and can accurately predict the performance for an urban macro-cell setup with commercial high-gain antennas.
Abstract: Channel models are important tools to evaluate the performance of new concepts in mobile communications. However, there is a tradeoff between complexity and accuracy. In this paper, we extend the popular Wireless World Initiative for New Radio (WINNER) channel model with new features to make it as realistic as possible. Our approach enables more realistic evaluation results at an early stage of algorithm development. The new model supports 3-D propagation, 3-D antenna patterns, time evolving channel traces of arbitrary length, scenario transitions and variable terminal speeds. We validated the model by measurements in a coherent LTE advanced testbed in downtown Berlin, Germany. We then reproduced the same scenario in the model and compared several channel parameters (delay spread, path gain, K-factor, geometry factor and capacity). The results match very well and we can accurately predict the performance for an urban macro-cell setup with commercial high-gain antennas. At the same time, the computational complexity does not increase significantly and we can use all existing WINNER parameter tables. These artificial channels, having equivalent characteristics as measured data, enable virtual field trials long before prototypes are available.
679 citations
01 Jan 2014
TL;DR: This article surveys the new trend of channel response in localization and investigates a large body of recent works and classify them overall into three categories according to how to use CSI, highlighting the differences between CSI and RSSI.
Abstract: The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.
612 citations
TL;DR: Practical open-loop and closed-loop training frameworks are proposed that offer better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.
Abstract: The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.
464 citations
TL;DR: It is shown that the secret key agreement capacity of a lossy and noisy optical channel assisted by unlimited two-way public classical communication is limited by an upper bound that is solely a function of the channel loss, regardless of how much optical power the protocol may use.
Abstract: An open question in quantum key distribution (QKD) is whether there exist protocols avoiding the exponential decay of the secret key generation rate with distance. Takeoka et al. show a fundamental tradeoff between the secret-key generation rate and the channel loss for optical repeater-less QKD protocols.
419 citations
TL;DR: JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information and a low-overhead probabilistic scheduling algorithm is proposed that selects the users at random with probabilities derived from large-system random matrix analysis.
Abstract: Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.
380 citations
TL;DR: This paper proposes two tight SM capacity upper bounds and presents the solution of the optimal time split ratio for the maximum system throughput according to the proposed upper bound and conducts Monte-carlo simulations to reveal the throughput gain of the proposed SM-FD relaying protocol.
Abstract: We consider a dual-hop full-duplex relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture, both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, the full-duplex relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode attains the highest throughput. However, compared to the delay-constrained transmission mode, the throughput gap is rather small. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay-constrained transmission mode depends only on the statistics of the channel, hence, is suitable for practical implementations.
374 citations
TL;DR: This letter proposes a solid analytical formulation and analyzes the signal metrics (attenuation and propagation delay) for molecular communication via diffusion channel with an absorbing receiver in a 3-D environment and proposes a model and the formulation that match well with the simulations without any normalization.
Abstract: Within the domain of molecular communications, researchers mimic the techniques in nature to come up with alternative communication methods for collaborating nanomachines. This letter investigates the channel transfer function for molecular communications via diffusion. In nature, information-carrying molecules are generally absorbed by the target node via receptors. Using the concentration function, without considering the absorption process, as the channel transfer function implicitly assumes that the receiver node does not affect the system. In this letter, we propose a solid analytical formulation and analyze the signal metrics (attenuation and propagation delay) for molecular communication via diffusion channel with an absorbing receiver in a 3-D environment. The proposed model and the formulation match well with the simulations without any normalization.
354 citations
TL;DR: This work provides concise and tight finite-key security bounds for practical decoy-state QKD that are valid against general attacks.
Abstract: Due to its ability to tolerate high channel loss, decoy-state quantum key distribution (QKD) has been one of the main focuses within the QKD community. Notably, several experimental groups have demonstrated that it is secure and feasible under real-world conditions. Crucially, however, the security and feasibility claims made by most of these experiments were obtained under the assumption that the eavesdropper is restricted to particular types of attacks or that the finite-key effects are neglected. Unfortunately, such assumptions are not possible to guarantee in practice. In this work, we provide concise and tight finite-key security bounds for practical decoy-state QKD that are valid against general attacks.
TL;DR: In this article, the authors considered a massive multiple-input-multiple-output (MIMO) system with a hybrid data-and-energy access point (H-AP) and multiple single-antenna users, where the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity.
Abstract: This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number $M$ of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame). To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-$M$ regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system. We define a metric, namely, the massive MIMO degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by $\log(M)$. We show that the proposed WET-MM system is optimal in terms of MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.
TL;DR: Cl clustering results for a double-directional 60 GHz MIMO channel model are presented and it is shown that the cluster angular characteristics are closely related to the room geometry and environment, making it infeasible to model the delay and angular domains independently.
Abstract: Efficient and realistic mm-wave channel models are of vital importance for the development of novel mm-wave wireless technologies. Though many of the current 60 GHz channel models are based on the useful concept of multipath clusters, only a limited number of 60 GHz channel measurements have been reported in the literature for this purpose. Therefore, there is still a need for further measurement based analyses of multipath clustering in the 60 GHz band. This paper presents clustering results for a double-directional 60 GHz MIMO channel model. Based on these results, we derive a model which is validated with measured data. Statistical cluster parameters are evaluated and compared with existing channel models. It is shown that the cluster angular characteristics are closely related to the room geometry and environment, making it infeasible to model the delay and angular domains independently. We also show that when using ray tracing to model the channel, it is insufficient to only consider walls, ceiling, floor and tables; finer structures such as ceiling lamps, chairs and bookshelves need to be taken into account as well.
TL;DR: This survey will first review traditional channel estimation approaches based on channel frequency response (CFR) and Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey.
Abstract: Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern wireless communication systems due to its robustness against the frequency selectivity of wireless channels. For coherent detection, channel estimation is essential for receiver design. Channel estimation is also necessary for diversity combining or interference suppression where there are multiple receive antennas. In this paper, we will present a survey on channel estimation for OFDM. This survey will first review traditional channel estimation approaches based on channel frequency response (CFR). Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey. Following the success of turbo codes and low-density parity check (LDPC) codes, iterative processing has been widely adopted in the design of receivers, and iterative channel estimation has received a lot of attention since that time. Iterative channel estimation will be emphasized in this survey as the emerging iterative receiver improves system performance significantly. The combination of multiple-input multiple-output (MIMO) and OFDM has been widely accepted in modern communication systems, and channel estimation in MIMO-OFDM systems will also be addressed in this survey. Open issues and future work are discussed at the end of this paper.
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TL;DR: This work investigates massive multiple-input-multiple output (MIMO) uplink systems with 1-bit analog-to-digital converters (ADCs) on each receiver antenna with good performance in terms of mutual information and symbol error rate (SER), and provides an analytical approach to calculate the Mutual information and SER of the MRC receiver.
Abstract: We investigate massive multiple-input-multiple output (MIMO) uplink systems with 1-bit analog-to-digital converters (ADCs) on each receiver antenna. Receivers that rely on 1-bit ADC do not need energy-consuming interfaces such as automatic gain control (AGC). This decreases both ADC building and operational costs. Our design is based on maximal ratio combining (MRC), zero-forcing (ZF), and least squares (LS) detection, taking into account the effects of the 1-bit ADC on channel estimation. Through numerical results, we show good performance of the system in terms of mutual information and symbol error rate (SER). Furthermore, we provide an analytical approach to calculate the mutual information and SER of the MRC receiver. The analytical approach reduces complexity in the sense that a symbol and channel noise vectors Monte Carlo simulation is avoided.
TL;DR: A new algorithm for pilot beam pattern design for optimal channel estimation under the assumption that the channel is a stationary Gauss-Markov random process that generates a sequentially-optimal sequence of pilot beam patterns with low complexity for a given set of system parameters.
Abstract: In this paper, the problem of pilot beam pattern design for channel estimation in massive multiple-input multiple-output systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation is proposed under the assumption that the channel is a stationary Gauss-Markov random process. The proposed algorithm designs the pilot beam pattern sequentially by exploiting the properties of Kalman filtering and the associated prediction error covariance matrices and also the channel statistics such as spatial and temporal channel correlation. The resulting design generates a sequentially-optimal sequence of pilot beam patterns with low complexity for a given set of system parameters. Numerical results show the effectiveness of the proposed algorithm.
TL;DR: This paper considers in this paper a multiuser MIMO WET system, and proposes a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval, and is able to estimate multi-antenna or multiple-input multiple-output channels simultaneously without reducing the analytic convergence speed.
Abstract: Multi-antenna or multiple-input multiple-output (MIMO) techniques are appealing to enhance the transmission efficiency and range for radio frequency (RF) signal enabled wireless energy transfer (WET). In order to reap the energy beamforming gain in MIMO WET, acquiring the channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation. To tackle this problem, we consider in this paper a multiuser MIMO WET system, and propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. Specifically, each feedback bit indicates the increase or decrease of the harvested energy by each ER in the present as compared to the previous intervals, which can be measured without changing the existing structure of the ER. Based on such feedback information, the ET adjusts transmit beamforming in subsequent training intervals and at the same time obtains improved estimates of the MIMO channels to different ERs by applying an optimization technique called analytic center cutting plane method (ACCPM). For the proposed ACCPM based channel learning algorithm, we analyze its worst-case convergence, from which it is revealed that the algorithm is able to estimate multiuser MIMO channels simultaneously without reducing the analytic convergence speed. Also, we provide extensive simulations to show its performances in terms of both convergence speed and energy transfer efficiency.
01 Dec 2014
TL;DR: A novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS), where both amplitude and phase information of CSI are extracted and shaped into sensitive metrics for target detection; and CSI across multi-antennas in MIMO systems are further exploited to improve the detection accuracy and robustness.
Abstract: Device-free passive detection is an emerging technology to detect whether there exists any moving entities in the area of interests without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, etc. Despite of the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to finer-grained channel descriptor at physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored full potentials of CSI for human detection. Moreover, space diversity supported by nowadays popular multi-antenna systems are not investigated to the comparable extent as frequency diversity. In this paper, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both amplitude and phase information of CSI are extracted and shaped into sensitive metrics for target detection; and CSI across multi-antennas in MIMO systems are further exploited to improve the detection accuracy and robustness. We prototype PADS on commercial WiFi devices and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.
TL;DR: This article provides an overview of the RF-powered CRNs and discusses the challenges that arise for dynamic spectrum access in these networks, and focuses on the trade-off among spectrum sensing, data transmission, and RF energy harvesting.
Abstract: Spectrum efficiency and energy efficiency are two critical issues in designing wireless networks. Through dynamic spectrum access, cognitive radios can improve the spectrum efficiency and capacity of wireless networks. On the other hand radio frequency (RF) energy harvesting has emerged as a promising technique to supply energy to wireless networks and thereby increase their energy efficiency. Therefore, to achieve both spectrum and energy efficiencies, the secondary users in a cognitive radio network (CRN) can be equipped with the RF energy harvesting capability, and such a network can be referred to as an RF-powered cognitive radio network. In this article we provide an overview of the RF-powered CRNs and discuss the challenges that arise for dynamic spectrum access in these networks. Focusing on the trade-off among spectrum sensing, data transmission, and RF energy harvesting, we then discuss the dynamic channel selection problem in a multi-channel RF-powered CRN. In the RF-powered CRN a secondary user can adaptively select a channel to transmit data when the channel is not occupied by any primary user. Alternatively, the secondary user can harvest RF energy for data transmission if the channel is occupied. The optimal channel selection policy of the secondary user can be obtained by formulating a Markov decision process (MDP) problem. We present some numerical results obtained by solving this MDP problem.
01 Nov 2014
TL;DR: This work proposes strategies for mmWave communications that exploit the inherent sparsity of mmWave channels in the angle and delay domains, and proposes the use of aperture shaping to ensure a sparse virtual-domain MIMO channel representation.
Abstract: We propose strategies for mmWave communications that exploit the inherent sparsity of mmWave channels in the angle and delay domains. In particular, we propose the use of aperture shaping to ensure a sparse virtual-domain MIMO channel representation; fast FFT-based modulation and demodulation schemes to expose the virtual-channel coefficients; a pilot design that facilitates fast LASSO-based sparse-channel estimation; and spectrally efficient precoding and decoding, via the Lanczos algorithm and waterfilling over both frequency and angle. Numerical experiments suggest that our approach comes close to achieving the perfect-CSI capacity of the mmWave channel.
TL;DR: Measurement results and model the propagation channel, in which a bus acts either as a shadowing object or as a relay between two passenger cars, are presented and a stochastic model is developed.
Abstract: Vehicle-to-vehicle (V2V) communication is an enabler for improved traffic safety and congestion control. As for any wireless system, the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel, in which a bus acts either as a shadowing object or as a relay between two passenger cars. The measurement setup is based on a Wireless Open-Access Research Platform (WARP) Field-Programmable Gate Array (FPGA) software radio as transmitter and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the path loss, shadowing, small-scale fading, delay spread, and cross correlation. The main effect of the bus is that it is acting as an obstruction creating an additional 15- to 20-dB attenuation and an increase in the root-mean-square delay spread by roughly 100 ns. A Nakagami distribution is found to well describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance dependence of the path loss is analyzed and a stochastic model is developed.
TL;DR: Approximate dynamic programming is employed to derive a family of tractable suboptimal communication policies exhibiting the same qualitative features as the optimal one, which is shown in simulations and is contrasted to other simple transmission policies.
Abstract: This paper considers the control of a linear plant when plant state information is being transmitted from a sensor to the controller over a wireless fading channel. The power allocated to these transmissions determines the probability of successful packet reception and is allowed to adapt online to both channel conditions and plant state. The goal is to design plant input and transmit power policies that minimize an infinite horizon cost combining power expenses and the conventional linear quadratic regulator control cost. Since plant inputs and transmit powers are in general coupled, a restricted information structure is imposed allowing them to be designed separately. Under this information structure the standard LQR controller becomes the optimal plant input policy, while the optimal communication policy follows a Markov decision process minimizing transmit power at the sensor and state estimation error at the controller. The optimal power adaptation to channel and plant states is examined qualitatively for general forward error correcting codes. In the particular case of capacity achieving codes event-triggered policies are recovered, where the sensor decides whether to transmit or not based on plant and channel conditions. Approximate dynamic programming is employed to derive a family of tractable suboptimal communication policies exhibiting the same qualitative features as the optimal one. The performance of our suboptimal policies is shown in simulations and is contrasted to other simple transmission policies.
TL;DR: In this paper, the authors explore design aspects of adaptive modulation based on orthogonal frequency division multiplexing (OFDM) for underwater acoustic (UWA) communications, and study its performance using real-time at-sea experiments.
Abstract: In this paper, we explore design aspects of adaptive modulation based on orthogonal frequency-division multiplexing (OFDM) for underwater acoustic (UWA) communications, and study its performance using real-time at-sea experiments. Our design criterion is to maximize the system throughput under a target average bit error rate (BER). We consider two different schemes based on the level of adaptivity: in the first scheme, only the modulation levels are adjusted while the power is allocated uniformly across the subcarriers, whereas in the second scheme, both the modulation levels and the power are adjusted adaptively. For both schemes we linearly predict the channel one travel time ahead so as to improve the performance in the presence of a long propagation delay. The system design assumes a feedback link from the receiver that is exploited in two forms: one that conveys the modulation alphabet and quantized power levels to be used for each subcarrier, and the other that conveys a quantized estimate of the sparse channel impulse response. The second approach is shown to be advantageous, as it requires significantly fewer feedback bits for the same system throughput. The effectiveness of the proposed adaptive schemes is demonstrated using computer simulations, real channel measurements recorded in shallow water off the western coast of Kauai, HI, USA, in June 2008, and real-time at-sea experiments conducted at the same location in July 2011. We note that this is the first paper that presents adaptive modulation results for UWA links with real-time at-sea experiments.
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TL;DR: In this paper, the authors studied the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the energy receiver.
Abstract: Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed \emph{energy beamforming}, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the \emph{net} harvested energy at the ER, which is the average harvested energy offset by that used for channel training. An optimization problem is formulated for the training design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when training should be employed to improve the net transferred energy in MIMO WET systems.
TL;DR: In this paper, the authors studied the amount of communication and auxiliary resources needed in both the classical and quantum cases, the tradeoffs among them, and the loss of simulation efficiency when auxiliary resources are absent or insufficient.
Abstract: Dual to the usual noisy channel coding problem, where a noisy (classical or quantum) channel is used to simulate a noiseless one, reverse Shannon theorems concern the use of noiseless channels to simulate noisy ones, and more generally the use of one noisy channel to simulate another. For channels of nonzero capacity, this simulation is always possible, but for it to be efficient, auxiliary resources of the proper kind and amount are generally required. In the classical case, shared randomness between sender and receiver is a sufficient auxiliary resource, regardless of the nature of the source, but in the quantum case, the requisite auxiliary resources for efficient simulation depend on both the channel being simulated, and the source from which the channel inputs are coming. For tensor power sources (the quantum generalization of classical memoryless sources), entanglement in the form of standard ebits (maximally entangled pairs of qubits) is sufficient, but for general sources, which may be arbitrarily correlated or entangled across channel inputs, additional resources, such as entanglement-embezzling states or backward communication, are generally needed. Combining existing and new results, we establish the amounts of communication and auxiliary resources needed in both the classical and quantum cases, the tradeoffs among them, and the loss of simulation efficiency when auxiliary resources are absent or insufficient. In particular, we find a new single-letter expression for the excess forward communication cost of coherent feedback simulations of quantum channels (i.e., simulations in which the sender retains what would escape into the environment in an ordinary simulation), on nontensor-power sources in the presence of unlimited ebits but no other auxiliary resource. Our results on tensor power sources establish a strong converse to the entanglement-assisted capacity theorem.
27 Oct 2014
TL;DR: A time-interleaved (TI) SAR ADC which enables background timing skew calibration without a separate timing reference channel and enhances the conversion speed of each SAR channel and incorporates a flash ADC operating at the full sampling rate of the TI ADC.
Abstract: SARs are one of the most energy-efficient ADC architectures for medium resolution and low-to-medium speed. To improve the limited bandwidth of SAR ADCs, the time-interleaved (TI) structure is often used [1,2]. However, TI ADCs have several issues caused by mismatches between channels, such as offset, gain, and timing-skew errors. Unlike the other errors, timing-skew causes errors that increase with input signal frequency. Considering that the TI structure is typically employed to increase bandwidth, timing-skew can be a dominant error source of TI ADCs. Recent works [1,3] have demonstrated a background timing-skew calibration using a dedicated additional channel as a timing reference. In this work, we present a TI SAR ADC that enables background timing-skew calibration without a separate timing reference channel and enhances the conversion speed of each channel.
Patent•
06 May 2014
TL;DR: In this article, the first and second devices are configured to switch to another wireless communication channel based at least in part on the signal strength of the probe signal for each of a plurality of potential wireless communication channels.
Abstract: A system embodiment includes a first device and a second device configured to communicate over a selected wireless communication channel selected from a band of channels or over a selected set of channels used in an adaptive frequency hopping scheme. The first device is configured to transmit a probe signal that has a plurality of frequencies contained within the band of channels, and the second device is configured to determine a signal strength of the probe signal for each of a plurality of potential communication channels within the band of channels. The first and second devices configured to switch to another wireless communication channel based at least in part on the signal strength of the probe signal for each of a plurality of potential wireless communication channels.
10 Jun 2014
TL;DR: It is demonstrated that the 3D correlation matrix can be well approximated by a Kronecker production of azimuth and elevation correlations, laying the theoretical support for the usage of a product codebook for reduced complexity feedback from the receiver to the transmitter.
Abstract: A 2D antenna array introduces a new level of control and additional degrees of freedom in multiple-input-multiple-output (MIMO) systems particularly for the so-called “massive MIMO” systems. To accurately assess the performance gains of these large arrays, existing azimuth-only channel models have been extended to handle 3D channels by modeling both the elevation and azimuth dimensions. In this paper, we study the channel correlation matrix of a generic ray-based 3D channel model, and our analysis and simulation results demonstrate that the 3D correlation matrix can be well approximated by a Kronecker production of azimuth and elevation correlations. This finding lays the theoretical support for the usage of a product codebook for reduced complexity feedback from the receiver to the transmitter. We also present the design of a product codebook based on Grassmannian line packing.
Patent•
09 Jan 2014
TL;DR: In this paper, the authors present a remote control system with which leakage current flowing in a switch can be reduced so that power consumption can be increased so that leakage current can be minimized.
Abstract: Provided is a remote control system with which leakage current flowing in a switch can be reduced so that power consumption can be reduced. The remote control system includes a portable information terminal, a server, and an electric device. The on/off of the switch included in the electric device is controlled using information transmitted from the portable information terminal to the server. The switch includes a transistor formed using a semiconductor whose band gap is larger than that of single crystal silicon in a channel formation region.