Showing papers on "Communications system published in 2018"
TL;DR: This paper builds, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios and open-source deep learning software libraries, and proposes a two-step learning procedure based on the idea of transfer learning that circumvents the challenges of training such a system over actual channels.
Abstract: End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios and open-source deep learning software libraries. We extend the existing ideas toward continuous data transmission, which eases their current restriction to short block lengths but also entails the issue of receiver synchronization. We overcome this problem by introducing a frame synchronization module based on another NN. A comparison of the BLER performance of the “learned” system with that of a practical baseline shows competitive performance close to $\text{1}$ dB, even without extensive hyperparameter tuning. We identify several practical challenges of training such a system over actual channels, in particular, the missing channel gradient, and propose a two-step learning procedure based on the idea of transfer learning that circumvents this issue.
757 citations
TL;DR: This paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications.
Abstract: Recently, ambient backscatter communication has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks, and thus it is expected to realize numerous Internet-of-Things applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.
650 citations
TL;DR: A survey of the mmWave propagation characteristics, channel modeling, and design guidelines, such as system and antenna design considerations for mmWave, including the link budget of the network, which are essential for mm Wave communication systems design is presented.
Abstract: The millimeter wave (mmWave) frequency band spanning from 30 to 300 GHz constitutes a substantial portion of the unused frequency spectrum, which is an important resource for future wireless communication systems in order to fulfill the escalating capacity demand. Given the improvements in integrated components and enhanced power efficiency at high frequencies, wireless systems can operate in the mmWave frequency band. In this paper, we present a survey of the mmWave propagation characteristics, channel modeling, and design guidelines, such as system and antenna design considerations for mmWave, including the link budget of the network, which are essential for mmWave communication systems. We commence by introducing the main channel propagation characteristics of mmWaves followed by channel modeling and design guidelines. Then, we report on the main measurement and modeling campaigns conducted in order to understand the mmWave band’s properties and present the associated channel models. We survey the different channel models focusing on the channel models available for the 28, 38, 60, and 73 GHz frequency bands. Finally, we present the mmWave channel model and its challenges in the context of mmWave communication systems design.
512 citations
TL;DR: This work focuses on a dual-functional multi-input-multi-output (MIMO) radar-communication system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously and proposes a branch-and-bound algorithm that obtains a globally optimal solution.
Abstract: We focus on a dual-functional multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously. Several design criteria are considered for minimizing the downlink multiuser interference. First, we consider both omnidirectional and directional beampattern design problems, where the closed-form globally optimal solutions are obtained. Based on the derived waveforms, we further consider weighted optimizations targeting a flexible tradeoff between radar and communications performance and introduce low-complexity algorithms. Moreover, to address the more practical constant modulus waveform design problem, we propose a branch-and-bound algorithm that obtains a globally optimal solution, and derive its worst-case complexity as function of the maximum iteration number. Finally, we assess the effectiveness of the proposed waveform design approaches via numerical results.
487 citations
TL;DR: The proposed IEEE 802.11ad-based radar meets the minimum accuracy/resolution requirement of range and velocity estimates for LRR applications and exploits the preamble of a single-carrier physical layer frame, which consists of Golay complementary sequences with good correlation properties that make it suitable for radar.
Abstract: Millimeter-wave (mmWave) radar is widely used in vehicles for applications such as adaptive cruise control and collision avoidance. In this paper, we propose an IEEE 802.11ad-based radar for long-range radar (LRR) applications at the 60 GHz unlicensed band. We exploit the preamble of a single-carrier physical layer frame, which consists of Golay complementary sequences with good correlation properties that make it suitable for radar. This system enables a joint waveform for automotive radar and a potential mmWave vehicular communication system based on the mmWave consumer wireless local area network standard, allowing hardware reuse. To formulate an integrated framework of vehicle-to-vehicle communication and LRR, we make typical assumptions for LRR applications, incorporating the full duplex radar operation. This new feature is motivated by the recent development of systems with sufficient isolation and self-interference cancellation. We develop single- and multi-frame radar receiver algorithms for target detection as well as range and velocity estimation for both single- and multi-target scenarios. Our proposed radar processing algorithms leverage channel estimation and time–frequency synchronization techniques used in a conventional IEEE 802.11ad receiver with minimal modifications. Analysis and simulations show that in a single-target scenario, a gigabits-per-second data rate is achieved simultaneously with cm-level range accuracy and cm/s-level velocity accuracy. The target vehicle is detected with a high probability (above 99.99 $\%$ ) at a low false alarm rate of 10 $^{-6}$ for an equivalent isotropically radiated power of 40 dBm up to a vehicle separation distance of about 200 m. The proposed IEEE 802.11ad-based radar meets the minimum accuracy/resolution requirement of range and velocity estimates for LRR applications.
469 citations
TL;DR: An overview of recent DSP developments for short-reach communications systems is presented and future trends are discussed.
Abstract: Driven primarily by cloud service and data-center applications, short-reach optical communication has become a key market segment and growing research area in recent years. Short-reach systems are characterized by direct detection-based receiver configurations and other low-cost and small form factor components that induce transmission impairments unforeseen in their coherent counterparts. Innovative signaling and digital signal processing (DSP) play a pivotal role in enabling these components to realize their ultimate potentials and meet data rate requirements in cost-effective manners. This paper presents an overview of recent DSP developments for short-reach communications systems and discusses future trends.
319 citations
TL;DR: This work considers detection based on deep learning, and shows it is possible to train detectors that perform well without any knowledge of the underlying channel models, and demonstrates that the bit error rate performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI.
Abstract: We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI) In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in real time as the signal stream arrives at the receiver We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model We also demonstrate that the bit error rate performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration
305 citations
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TL;DR: The applications of DL in physical layer communications are categorized into systems with and without block structures, and the power ofDL in signal compression and signal detection is demonstrated.
Abstract: Deep learning (DL) has shown the great potentials to break the bottleneck of communication systems. This article provides an overview on the recent advancements in DL-based physical layer communications. DL can improve the performance of each individual block in communication systems or optimize the whole transmitter/receiver. Therefore, we categorize the applications of DL in physical layer communications into systems with and without block structures. For DL-based communication systems with block structures, we demonstrate the power of DL in signal compression and signal detection. We also discuss the recent endeavors in developing end-to-end communication systems. Finally, the potential research directions are identified to boost the intelligent physical layer communications with DL.
281 citations
01 Dec 2018
TL;DR: In this article, the suitability of LIS for green communications in terms of energy efficiency was investigated, which is expressed as the number of bits per Joule, and the transmit powers per user and the values for the surface elements that jointly maximize the system's EE performance.
Abstract: We consider a multi-user Multiple-Input Single-Output (MISO) communication system comprising of a multiantenna base station communicating in the downlink simultaneously with multiple single-antenna mobile users. This communication is assumed to be assisted by a Large Intelligent Surface (LIS) that consists of many nearly passive antenna elements, whose parameters can be tuned according to desired objectives. The latest design advances on these surfaces suggest cheap elements effectively acting as low resolution (even 1-bit resolution) phase shifters, whose joint configuration affects the electromagnetic behavior of the wireless propagation channel. In this paper, we investigate the suitability of LIS for green communications in terms of Energy Efficiency (EE), which is expressed as the number of bits per Joule. In particular, for the considered multi-user MISO system, we design the transmit powers per user and the values for the surface elements that jointly maximize the system's EE performance. Our representative simulation results show that LIS-assisted communication, even with nearly passive 1-bit resolution antenna elements, provides significant EE gains compared to conventional relay-assisted communication.
281 citations
TL;DR: In this article, an end-to-end deep learning-based optimization of optical fiber communication systems is proposed to achieve bit error rates below the 6.7% hard-decision forward error correction (HD-FEC) threshold.
Abstract: In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applying it to intensity modulation/direct detection (IM/DD) systems and show that we can achieve bit error rates below the 6.7% hard-decision forward error correction (HD-FEC) threshold. We model all componentry of the transmitter and receiver, as well as the fiber channel, and apply deep learning to find transmitter and receiver configurations minimizing the symbol error rate. We propose and verify in simulations a training method that yields robust and flexible transceivers that allow—without reconfiguration—reliable transmission over a large range of link dispersions. The results from end-to-end deep learning are successfully verified for the first time in an experiment. In particular, we achieve information rates of 42 Gb/s below the HD-FEC threshold at distances beyond 40 km. We find that our results outperform conventional IM/DD solutions based on two- and four-level pulse amplitude modulation with feedforward equalization at the receiver. Our study is the first step toward end-to-end deep learning based optimization of optical fiber communication systems.
274 citations
TL;DR: Caching has been studied for more than 40 years and has recently received increased attention from industry and academia as mentioned in this paper, with the following goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks.
Abstract: This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this special issue. We analyze the challenges that caching needs to address today, also considering an industry perspective, and identify bottleneck issues that must be resolved to unleash the full potential of this promising technique.
TL;DR: This survey categorizes ATP mechanisms according to their working principles, use cases, and implementation technology to discuss advantages and disadvantages of the surveyed ATP mechanisms, and presents a discussion on challenges and future research.
Abstract: This paper presents a comprehensive survey on acquisition, tracking, and pointing (ATP) mechanisms used in free-space optical (FSO) communications systems. ATP mechanisms are a critical component for a wide variety of use cases of mobile FSO communications. ATP mechanisms are used to align an FSO transmitter and receiver to attain line-of-sight, which is required for effective operation of FSO communications. Transceiver motion is not only associated to mobile stations but also to temporary displacements experienced by stationary FSO terminals, such as in building-to-building FSO communications. This survey categorizes ATP mechanisms according to their working principles, use cases, and implementation technology. This paper also discusses advantages and disadvantages of the surveyed ATP mechanisms, and presents a discussion on challenges and future research.
TL;DR: An iterative suboptimal algorithm is proposed to solve a UAV-ground communication system with multiple potential eavesdroppers on the ground by jointly designing the robust trajectory and transmit power of the UAV over a given flight duration by exploiting the mobility of UAV via its trajectory design.
Abstract: Unmanned aerial vehicles (UAVs) are anticipated to be widely deployed in future wireless communications, due to their advantages of high mobility and easy deployment. However, the broadcast nature of air-to-ground line-of-sight wireless channels brings a new challenge to the information security of UAV-ground communication. This paper tackles such a challenge in the physical layer by exploiting the mobility of UAV via its trajectory design. We consider a UAV-ground communication system with multiple potential eavesdroppers on the ground, where the information on the locations of the eavesdroppers is imperfect. We formulate an optimization problem, which maximizes the average worst case secrecy rate of the system by jointly designing the robust trajectory and transmit power of the UAV over a given flight duration. The nonconvexity of the optimization problem and the imperfect location information of the eavesdroppers make the problem difficult to be solved optimally. We propose an iterative suboptimal algorithm to solve this problem efficiently by applying the block coordinate descent method, $\mathcal {S}$ -procedure, and successive convex optimization method. Simulation results show that the proposed algorithm can improve the average worst case secrecy rate significantly, as compared to two other benchmark algorithms without robust design.
TL;DR: Numerical simulations demonstrate that the radar transmitted power can be efficiently reduced by exploiting the communication signals scattered off the target at the radar receiver, and it is shown that the robust waveforms bound the worst-case power-saving performance of radar system for any target spectra in the uncertainty sets.
Abstract: This paper considers the problem of power minimization-based robust orthogonal frequency division multiplexing (OFDM) radar waveform design, in which the radar coexists with a communication system in the same frequency band. Recognizing that the precise characteristics of target spectra are impossible to capture in practice, it is assumed that the target spectra lie in uncertainty sets bounded by known upper and lower bounds. Based on this uncertainty model, three different power minimization-based robust radar waveform design criteria are proposed to minimize the worst-case radar transmitted power by optimizing the OFDM radar waveform, which are constrained by a specified mutual information requirement for target characterization and a minimum capacity threshold for communication system. These criteria differ in the way the communication signals scattered off the target are considered: 1) as useful energy, 2) as interference, or 3) ignored altogether at the radar receiver. Numerical simulations demonstrate that the radar transmitted power can be efficiently reduced by exploiting the communication signals scattered off the target at the radar receiver. It is also shown that the robust waveforms bound the worst-case power-saving performance of radar system for any target spectra in the uncertainty sets.
02 Jul 2018
TL;DR: An end-to-end wireless communication system in which DNNs are employed for all signal-related functionalities, including encoding, decoding, modulation, and equalization is developed, in which accurate instantaneous channel transfer function is necessary to compute the gradient of the DNN representing.
Abstract: In this article, we use deep neural networks (DNNs) to develop an end-to-end wireless communication system, in which DNNs are employed for all signal-related functionalities, including encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, i.e., the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to obtain accurate channel state information for signal detection at the receiver, the received signal corresponding to the pilot data is added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.
TL;DR: A new integration architecture of the cloud, MEC, and IoT is presented, and a lightweight request and admission framework is proposed to resolve the scalability problem and satisfy the latency requirements of different services and reduce the energy consumption of IoT devices.
Abstract: Mobile edge computing provides the radio access networks with cloud computing capabilities to fulfill the requirements of the Internet of Things services such as high reliability and low latency. Offloading services to edge servers can alleviate the storage and computing limitations and prolong the lifetimes of the IoT devices. However, offloading in MEC faces scalability problems due to the massive number of IoT devices. In this article, we present a new integration architecture of the cloud, MEC, and IoT, and propose a lightweight request and admission framework to resolve the scalability problem. Without coordination among devices, the proposed framework can be operated at the IoT devices and computing servers separately, by encapsulating latency requirements in offloading requests. Then a selective offloading scheme is designed to minimize the energy consumption of devices, where the signaling overhead can be further reduced by enabling the devices to be self-nominated or self-denied for offloading. Simulation results show that our proposed selective offloading scheme can satisfy the latency requirements of different services and reduce the energy consumption of IoT devices.
TL;DR: A novel hybrid transmitter design is proposed by combining the advantages of both ambient backscatter and wireless powered communications by introducing a multiple access scheme to coordinate hybrid data transmissions in the cognitive radio environment.
Abstract: Ambient backscatter communication technology has been introduced recently, and is quickly becoming a promising choice for self-sustainable communication systems, as an external power supply or a dedicated carrier emitter is not required. By leveraging existing RF signal resources, ambient backscatter technology can support sustainable and independent communications and consequently open up a whole new set of applications that facilitate Internet of things (IoT). In this article, we study an integration of ambient backscatter with wireless powered communication networks (WPCNs). We first present an overview of backscatter communication systems with an emphasis on the emerging ambient backscatter technology. Then we propose a novel hybrid transmitter design by combining the advantages of both ambient backscatter and wireless powered communications. Furthermore, in the cognitive radio environment, we introduce a multiple access scheme to coordinate hybrid data transmissions. The performance evaluation shows that the hybrid transmitter outperforms traditional designs. In addition, we discuss open issues related to ambient backscatter networking.
TL;DR: The role of future 5G V2X systems in enabling more efficient vehicular transportation is discussed, from improved traffic flow and reduced intervehicle spacing on highways to coordinated intersections in cities to automated smart parking, all of which will ultimately enable seamless end-to-end personal mobility.
Abstract: The ultimate goal of next-generation vehicle-toeverything (V2X) communication systems is enabling accident-free, cooperative automated driving that uses the available roadway efficiently. To achieve this goal, the communication system will need to enable a diverse set of use cases, each with a specific set of requirements. We discuss the main usecase categories, analyze their requirements, and compare them against the capabilities of currently available communication technologies. Based on the analysis, we identify a gap and indicate possible system designs for the fifth-generation (5G) V2X that could close the gap. Furthermore, we discuss an architecture of the 5G V2X radio access network (RAN) that incorporates diverse communication technologies, including current and cellular systems in centimeter wave (cm-wave) and millimeter wave (mm-wave), IEEE Standard 802.11p [1], and vehicular visible light communications (VVLC). Finally, we discuss the role of future 5G V2X systems in enabling more efficient vehicular transportation: from improved traffic flow and reduced intervehicle spacing on highways to coordinated intersections in cities (the cheapest way to increasing the road capacity) to automated smart parking (no more visits to the parking garage!), all of which will ultimately enable seamless end-to-end personal mobility.
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TL;DR: In this paper, the authors investigated the resource allocation algorithm design for multicarrier solar-powered UAV communication systems, where the UAV is powered by solar energy enabling sustainable communication services to multiple ground users.
Abstract: In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the three-dimensional (3D) aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an offline resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service (QoS) requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on online algorithm design which only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the offline scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on successive convex approximation. Our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descents again to a lower altitude to reduce the path loss of the communication links to the users it serves.
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TL;DR: This paper designs the transmit powers per user and the values for the surface elements that jointly maximize the system's EE performance, and shows that LIS- assisted communication, even with nearly passive 1-bit resolution antenna elements, provides significant EE gains compared to conventional relay-assisted communication.
Abstract: We consider a multi-user Multiple-Input Single-Output (MISO) communication system comprising of a multi-antenna base station communicating in the downlink simultaneously with multiple single-antenna mobile users. This communication is assumed to be assisted by a Large Intelligent Surface (LIS) that consists of many nearly passive antenna elements, whose parameters can be tuned according to desired objectives. The latest design advances on these surfaces suggest cheap elements effectively acting as low resolution (even $1$-bit resolution) phase shifters, whose joint configuration affects the electromagnetic behavior of the wireless propagation channel. In this paper, we investigate the suitability of LIS for green communications in terms of Energy Efficiency (EE), which is expressed as the number of bits per Joule. In particular, for the considered multi-user MISO system, we design the transmit powers per user and the values for the surface elements that jointly maximize the system's EE performance. Our representative simulation results show that LIS-assisted communication, even with nearly passive $1$-bit resolution antenna elements, provides significant EE gains compared to conventional relay-assisted communication.
TL;DR: This paper considers the joint design of a multiple-input multiple-output (MIMO) radar with co-located antennas and a MIMO communication system, and a reduced-complexity iterative algorithm, based on iterative alternating maximization of three suitably designed subproblems, is proposed and analyzed.
Abstract: This paper considers the joint design of a multiple-input multiple-output (MIMO) radar with co-located antennas and a MIMO communication system. The degrees of freedom under the designer's control are the waveforms transmitted by the radar transmit array, the filter at the radar array and the code-book employed by the communication system to form its space-time code matrix. Two formulations of the spectrum sharing problem are proposed. First, the design problem is stated as the constrained maximization of the signal-to-interference-plus-noise ratio at the radar receiver, where interference is due to both clutter and the coexistence structure, and the constraints concern both the similarity with a standard radar waveform and the rate achievable by the communication system, on top of those on the transmit energy. The resulting problem is nonconvex, but a reduced-complexity iterative algorithm, based on iterative alternating maximization of three suitably designed subproblems, is proposed and analyzed. In addition, the constrained maximization of the communication rate is also investigated. The convergence of all the devised algorithms is guaranteed. Finally, a thorough performance assessment is presented, aimed at showing the merits of the proposed approach.
25 Jun 2018
TL;DR: This work extends the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP) and shows that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training.
Abstract: We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely single-tap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations. This enables reliable communication over multipath channels and makes the communication scheme suitable for commodity hardware with imprecise oscillators. We show that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training. We compare the performance of the autoencoder-based system against that of a state-of-the-art OFDM baseline over frequency-selective fading channels. Finally, the impact of a non-linear amplifier is investigated and we show that the autoencoder inherently learns how to deal with such hardware impairments.
TL;DR: Numerical results show that the proposed method achieves a significant power saving compared to conventional approaches, while obtaining a favorable performance-complexity tradeoff.
Abstract: We propose a novel approach to enable the coexistence between Multi-Input-Multi-Output (MIMO) radar and downlink multiuser multi-input single-output communication system. By exploiting the constructive multiuser interference (MUI), the proposed approach tradeoff useful MUI power for reducing the transmit power, to obtain a power efficient transmission. This paper focuses on two optimization problems: a) Transmit power minimization at the base station (BS), while guaranteeing the receive signal-to-interference-plus-noise ratio (SINR) level of downlink users and the interference-to-noise ratio level to radar; b) Minimization of the interference from BS to radar for a given requirement of downlink SINR and transmit power budget. To reduce the computational overhead of the proposed scheme in practice, an algorithm based on gradient projection is designed to solve the power minimization problem. In addition, we investigate the tradeoff between the performance of radar and communication, and analytically derive the key metrics for MIMO radar in the presence of the interference from the BS. Finally, a robust power minimization problem is formulated to ensure the effectiveness of the proposed method in the case of imperfect channel state information. Numerical results show that the proposed method achieves a significant power saving compared to conventional approaches, while obtaining a favorable performance-complexity tradeoff.
TL;DR: Simulation results show that the proposed radio resource management scheme can reduce the interference from V 2V communication to CUEs and ensure the latency and reliability requirements of V2V communication.
Abstract: By leveraging direct device-to-device interaction, LTE vehicle-to-vehicle (V2V) communication becomes a promising solution to meet the stringent requirements of vehicular communication. In this paper, we propose jointly optimizing the radio resource, power allocation, and modulation/coding schemes of the V2V communications, in order to guarantee the latency and reliability requirements of vehicular user equipments (VUEs) while maximizing the information rate of cellular user equipment (CUE). To ensure the solvability of this optimization problem, the packet latency constraint is first transformed into a data rate constraint based on random network analysis by adopting the Poisson distribution model for the packet arrival process of each VUE. Then, utilizing the Lagrange dual decomposition and binary search, a resource management algorithm is proposed to find the optimal solution of joint optimization problem with reasonable complexity. Simulation results show that the proposed radio resource management scheme can reduce the interference from V2V communication to CUEs and ensure the latency and reliability requirements of V2V communication.
TL;DR: This paper provides closed-form solutions for the optimum transmit policies for both systems under two basic models for the scattering produced by the radar onto the communication receiver, and account for possible correlation of the signal-independent fraction of the interference impinging on the radar.
Abstract: The focus of this paper is on coexistence between a communication system and a pulsed radar sharing the same bandwidth. Based on the fact that the interference generated by the radar onto the communication receiver is intermittent and depends on the density of scattering objects (such as, e.g., targets), we first show that the communication system is equivalent to a set of independent parallel channels, whereby precoding on each channel can be introduced as a new degree of freedom. We introduce a new figure of merit, named the compound rate , which is a convex combination of rates with and without interference, to be optimized under constraints concerning the signal-to-interference-plus-noise ratio (including signal-dependent interference due to clutter) experienced by the radar and obviously the powers emitted by the two systems: the degrees of freedom are the radar waveform and the aforementioned encoding matrix for the communication symbols. We provide closed-form solutions for the optimum transmit policies for both systems under two basic models for the scattering produced by the radar onto the communication receiver, and account for possible correlation of the signal-independent fraction of the interference impinging on the radar. We also discuss the region of the achievable communication rates with and without interference. A thorough performance assessment shows the potentials and the limitations of the proposed co-existing architecture.
TL;DR: This article proposes to improve the performance of the existing relay-assisted FSO systems by relaxing both of these highly restrictive assumptions through the integration of UAVs as buffer-aided moving relays into the conventional relay- assistance systems.
Abstract: Relay-assisted FSO systems were proposed as a means for remedying the effects of the various atmospheric impairments on the quality of the FSO signal. Conventional relay-assisted FSO systems, however, are designed around two basic assumptions: relays are buffer-free, and relays are stationary. This article proposes to improve the performance of the existing relay-assisted FSO systems by relaxing both of these highly restrictive assumptions through the integration of UAVs as buffer-aided moving relays into the conventional relay-assisted FSO systems. Specifically, two possible simple integration scenarios are proposed and analyzed through simulation. The obtained simulation results demonstrate the great potential associated with the proposed highly promising, innovative, hybrid FSO architecture. Given that high performance gains are observed under small buffer sizes, it becomes conceivable to employ buffer-aided moving relaying UAVs to serve a variety of other purposes. This includes, for instance, having these UAVs oversee the operation of amateur drones for potential misbehavior or wrongdoing within the area of their deployment.
01 Oct 2018
TL;DR: In this paper, an end-to-end RL-based autoencoder was proposed to learn a communication system over any type of channel without prior assumptions, including additive white Gaussian noise (AWGN) and Rayleigh block-fading (RBF).
Abstract: The idea of end-to-end learning of communications systems through neural network (NN)-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm iterates between supervised training of the receiver and reinforcement learning (RL)-based training of the transmitter. We demonstrate that this approach works as well as fully supervised methods on additive white Gaussian noise (AWGN) and Rayleigh block-fading (RBF) channels. Surprisingly, while our method converges slower on AWGN channels than supervised training, it converges faster on RBF channels. Our results are a first step towards learning of communications systems over any type of channel without prior assumptions.
TL;DR: This paper proposes a blind beam tracking approach for Ka-band UAV-satellite communication system, where UAV is equipped with a hybrid large-scale antenna array, where an array structure based simultaneous perturbation algorithm is designed.
Abstract: Unmanned aerial vehicle (UAV)-satellite communication has drawn dramatic attention for its potential to build the integrated space-air-ground network and the seamless wide-area coverage. A key challenge to UAV-satellite communication is its unstable beam pointing due to the UAV navigation, which is a typical SatCom on-the-move scenario. In this paper, we propose a blind beam tracking approach for Ka-band UAV-satellite communication system, where UAV is equipped with a hybrid large-scale antenna array. The effects of UAV navigation are firstly released through the mechanical adjustment, which could approximately point the beam towards the target satellite through beam stabilization and dynamic isolation . Specially, the attitude information for mechanical adjustment can be realtimely derived from data fusion of low-cost sensors. Then, the precision of beam pointing is blindly refined through electrically adjusting the weight of the massive antennas, where an array structure based simultaneous perturbation algorithm is designed. Simulation results are provided to demonstrate the superiority of the proposed method over the existing ones.
TL;DR: This paper describes the different beamforming approaches in each network topology according to its design objective such as increasing the throughput, enhancing the energy transfer efficiency, and minimizing the total transmit power, with paying special attention to exploiting the physical layer security.
Abstract: Wireless energy harvesting (EH) is a promising solution to prolong lifetime of power-constrained networks such as military and sensor networks. The high sensitivity of energy transfer to signal decay due to path loss and fading, promotes multi-antenna techniques like beamforming as the candidate transmission scheme for EH networks. Exploiting beamforming in EH networks has gained overwhelming interest, and lot of literature has appeared recently regarding this topic. The objective of this paper is to point out the state-of-the-art research activity on beamforming implementation in EH wireless networks. We first review the basic concepts and architecture of EH wireless networks. In addition, we also discuss the effects of beamforming transmission scheme on system performance in EH wireless communication. Furthermore, we present a comprehensive survey of multi-antenna EH communications. We cover the supporting network architectures like broadcasting, relay, and cognitive radio networks with the various beamforming deployment within the network architecture. We classify the different beamforming approaches in each network topology according to its design objective such as increasing the throughput, enhancing the energy transfer efficiency, and minimizing the total transmit power, with paying special attention to exploiting the physical layer security. We also survey major advances as well as open issues, challenges, and future research directions in multi-antenna EH communications.
TL;DR: The sparse and the location-dependent properties constitute the most important and distinctive characteristics of the maritime wireless channels, and are highlighted in a thorough review of existing modeling approaches and measurement campaigns.
Abstract: Recently, broadband maritime communication has attracted much attention due to the rapid development of blue economy. In addition to the conventional MF/HF/VHF bands, there has been increasing interests in the utilization of higher frequency bands to provide broadband data service to the sea area. To design efficient maritime communication systems, the first and a fundamental requirement is to develop a framework to understand the wireless channels. In an integrated air-ground-sea communications network, there are two major type of channels to be investigated, namely the air-to-sea channel (e.g., for communication links from the aircraft-based base stations or relays) and the near-sea-surface channel (for land-to-ship/ship-to-land or ship-to-ship communications). Due to the unique features of the maritime propagation environment such as sparse scattering, sea wave movement, and the ducting effect over the sea surface, the modeling of these maritime channel links differs from conventional terrestrial wireless channels in many aspects and, consequently, will result in significant impact on the transceiver design. In this survey, we highlight the most notable differences from the modeling perspective as well as the channel characteristics for the air-to-sea and near-sea-surface channel links, with more focus on the latter. After a thorough review of existing modeling approaches and measurement campaigns, we conclude that the sparse and the location-dependent properties constitute the most important and distinctive characteristics of the maritime wireless channels. As such, we further remark on the challenges and research topics for future development of maritime communications.