Showing papers in "IEEE Wireless Communications Letters in 2018"
TL;DR: The proposed deep learning-based approach to handle wireless OFDM channels in an end-to-end manner is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists.
Abstract: This letter presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we exploit deep learning to handle wireless OFDM channels in an end-to-end manner. Different from existing OFDM receivers that first estimate channel state information (CSI) explicitly and then detect/recover the transmitted symbols using the estimated CSI, the proposed deep learning-based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from simulation based on channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach can address channel distortion and detect the transmitted symbols with performance comparable to the minimum mean-square error estimator. Furthermore, the deep learning-based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortion and interference.
1,357 citations
TL;DR: The learned denoising-based approximate message passing (LDAMP) network is exploited and significantly outperforms state-of-the-art compressed sensing-based algorithms even when the receiver is equipped with a small number of RF chains.
Abstract: Channel estimation is very challenging when the receiver is equipped with a limited number of radio-frequency (RF) chains in beamspace millimeter-wave massive multiple-input and multiple-output systems. To solve this problem, we exploit a learned denoising-based approximate message passing (LDAMP) network. This neural network can learn channel structure and estimate channel from a large number of training data. Furthermore, we provide an analytical framework on the asymptotic performance of the channel estimator. Based on our analysis and simulation results, the LDAMP neural network significantly outperforms state-of-the-art compressed sensing-based algorithms even when the receiver is equipped with a small number of RF chains.
587 citations
TL;DR: This letter jointly optimize the SNs’ wake-up schedule and UAV’s trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN.
Abstract: In wireless sensor networks, utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. In this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs’ wake-up schedule and UAV’s trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.
527 citations
TL;DR: In this article, a deep learning-based CSI sensing and recovery mechanism is proposed to learn to effectively use channel structure from training samples, which can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing-based methods.
Abstract: In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.
513 citations
TL;DR: This letter is the first in literature that studies a novel 3-D UAV-BS placement that maximizes the number of covered users with different quality-of-service requirements and proposes a low-complexity algorithm, namely, maximal weighted area (MWA) algorithm to tackle the placement problem.
Abstract: The need for a rapid-to-deploy solution for providing wireless cellular services can be realized by unmanned aerial vehicle base stations (UAV-BSs). To the best of our knowledge, this letter is the first in literature that studies a novel 3-D UAV-BS placement that maximizes the number of covered users with different quality-of-service requirements. We model the placement problem as a multiple circles placement problem and propose an optimal placement algorithm that utilizes an exhaustive search (ES) over a 1-D parameter in a closed region. We also propose a low-complexity algorithm, namely, maximal weighted area (MWA) algorithm to tackle the placement problem. Numerical simulations are presented showing that the MWA algorithm performs very close to the ES algorithm with a significant complexity reduction.
403 citations
TL;DR: An initial insight on the radio propagation characteristics of cellular-to-UAV (CtU) channel is provided and the statistical behavior of the path-loss from a cellular base station toward a flying UAV is model.
Abstract: Operating unmanned aerial vehicle (UAV) over cellular networks would open the barriers of remote navigation and far-flung flying by combining the benefits of UAVs and the ubiquitous availability of cellular networks. In this letter, we provide an initial insight on the radio propagation characteristics of cellular-to-UAV (CtU) channel. In particular, we model the statistical behavior of the path-loss from a cellular base station toward a flying UAV. Where we report the value of the path-loss as a function of the depression angle and the terrestrial coverage beneath the UAV. The provided model is derived based on extensive experimental data measurements conducted in a typical suburban environment for both terrestrial (by drive test) and aerial coverage (using a UAV). The model provides simple and accurate prediction of CtU path-loss that can be useful for both researchers and network operators alike.
237 citations
TL;DR: In this article, a Stackelberg game is formulated to model the interaction between the edge cloud and users, where the edge Cloud sets prices to maximize its revenue subject to its finite computation capacity, and for given prices, each user locally makes offloading decision to minimize its own cost which is defined as latency plus payment.
Abstract: Mobile-edge computing is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to network edge. In this letter, we propose a price-based distributed method to manage the offloaded computation tasks from users. A Stackelberg game is formulated to model the interaction between the edge cloud and users, where the edge cloud sets prices to maximize its revenue subject to its finite computation capacity, and for given prices, each user locally makes offloading decision to minimize its own cost which is defined as latency plus payment. Depending on the edge cloud’s knowledge of the network information, we develop the uniform and differentiated pricing algorithms, which can both be implemented in distributed manners. Simulation results validate the effectiveness of the proposed schemes.
202 citations
TL;DR: Machine learning models that jointly explore the spatio-temporal correlations are proposed and several recurrent neural network structures are utilized to explore the commonalities and differences across cells in improving the prediction performance.
Abstract: Accurate prediction of user traffic in cellular networks is crucial to improve the system performance in terms of energy efficiency and resource utilization. However, existing work mainly considers the temporal traffic correlation within each cell while neglecting the spatial correlation across neighboring cells. In this letter, machine learning models that jointly explore the spatio-temporal correlations are proposed. Specifically, several recurrent neural network structures are utilized. Furthermore, a multi-task learning approach is adopted to explore the commonalities and differences across cells in improving the prediction performance. Base on real data, we demonstrate the benefits of joint learning over spatial and temporal dimensions.
118 citations
TL;DR: A contention-based transmission scheme aimed at users with small payloads is proposed to reduce collision probability by considering multiple transmissions for the same packet for reliable reception and achieves target reliability within the latency window.
Abstract: We consider a sporadic ultra-reliable and low latency communications in the uplink 5G cellular systems. Reliable low latency access for randomly emerging packet transmission cannot be guaranteed in current wireless systems. To achieve the goal of low latency and high reliability simultaneously, we propose a contention-based transmission scheme aimed at users with small payloads. We seek to reduce collision probability by considering multiple transmissions for the same packet for reliable reception. We find the optimal number of consecutive multiple transmissions that reduces collisions and achieves target reliability within the latency window. Performance is analyzed with a frame structure planned for 5G cellular systems. Results are compared with default multi-channel slotted ALOHA access scheme.
117 citations
TL;DR: In this article, the authors study the dynamics of mining pool selection in a blockchain network, where mining pools may choose arbitrary block mining strategies and identify the hash rate for puzzle-solving and the block propagation delay as two major factors determining the mining competition results.
Abstract: In proof-of-work-based blockchain networks, the block miners participate in a crypto-puzzle solving competition to win the reward of publishing (i.e., mining) new blocks. Due to the remarkable difficulty of the crypto-puzzle, individual miners tend to join mining pools to secure stable profits. We study the dynamics of mining pool selection in a blockchain network, where mining pools may choose arbitrary block mining strategies. We identify the hash rate for puzzle-solving and the block propagation delay as two major factors determining the mining competition results. We then model the strategy evolution of individual miners as an evolutionary game. We provide the theoretical analysis of evolutionary stability in the pool selection dynamics for a two-pool case. Numerical simulations support our theoretical findings as well as demonstrate the stability in the evolution of miners’ strategies in a general case.
115 citations
TL;DR: In this article, the authors present a discrete-time formulation of an orthogonal frequency division multiplexing-based OTFS system, where they argue against deployment of window functions at the transmitter in realistic scenarios and thus limit any sort of windowing to the receiver.
Abstract: Orthogonal time frequency space (OTFS) modulation is a 2-D signaling technique that has recently emerged in the literature to tackle the time-varying (TV) wireless channels. OTFS deploys the Doppler-delay plane to multiplex the transmit data where the time variations of the TV channel are integrated over time and hence the equivalent channel relating the input and output of the system boils down to a time-invariant one. This signaling technique can be implemented on the top of a given multicarrier waveform with the addition of precoding and post-processing units to the modulator and demodulator. In this letter, we present discrete-time formulation of an orthogonal frequency division multiplexing-based OTFS system. We argue against deployment of window functions at the OTFS transmitter in realistic scenarios and thus limit any sort of windowing to the receiver side. We study the channel impact in discrete-time providing deeper insights into OTFS systems. Moreover, our derivations lead to simplified modulator and demodulator structures that are far simpler than those in the literature.
TL;DR: A novel stochastic geometry-based network planning approach that focuses on the structure of the network to find strategic placement for multiple UAV-BSs in a large-scale network is proposed.
Abstract: Using base stations mounted on an unmanned aerial vehicle (UAV-BSs) is a promising new evolution of wireless networks for the provision of on-demand high data rates. While many studies have explored deploying UAV-BSs in a green field—no existence of terrestrial BSs, this letter focuses on the deployment of UAV-BSs in the presence of a terrestrial network. The purpose of this letter is twofold: 1) to provide supply-side estimation for how many UAV-BSs are needed to support a terrestrial network so as to achieve a particular quality of service and 2) to investigate where these UAV-BSs should hover. We propose a novel stochastic geometry-based network planning approach that focuses on the structure of the network to find strategic placement for multiple UAV-BSs in a large-scale network.
TL;DR: This letter investigates the effect of non-parallel misalignment on the channel capacity of the RF-OAM communication system equipped with uniform circular array and proposes a transmit/receive beam steering approach to circumvent the large performance degradation.
Abstract: Radio frequency-orbital angular momentum (RF-OAM) is a technique that provides extra degrees of freedom to improve spectrum efficiency of wireless communications However, OAM requires perfect alignment of the transmit and the receive antennas and this harsh precondition greatly challenges practical applications of RF-OAM In this letter, we first investigate the effect of non-parallel misalignment on the channel capacity of the RF-OAM communication system equipped with uniform circular array Then, we propose a transmit/receive beam steering approach to circumvent the large performance degradation in not only non-parallel case, but also off-axis and other general misalignment cases The effectiveness of the beam steering approach is validated through both mathematical analysis and numerical simulations
TL;DR: A linear low-resolution-aware minimum mean square error detector for soft multiuser interference mitigation and a quasi-uniform quantizer with scaling factors is devised to lower the error floor of low-density parity-check codes.
Abstract: We present a novel iterative detection and decoding scheme for uplink large-scale multiuser multiple-antenna systems. In order to reduce the receiver’s energy consumption and computational complexity, 1-bit analog-to-digital converters are used in the front-end. The performance loss due to the 1-bit quantization can be mitigated by using large-scale antenna arrays. We propose a linear low-resolution-aware minimum mean square error detector for soft multiuser interference mitigation. Moreover, short block length low-density parity-check codes are considered for avoiding high latency. In the channel decoder, a quasi-uniform quantizer with scaling factors is devised to lower the error floor of low-density parity-check codes. Simulations show good performance of the system in terms of bit error rate as compared to prior work.
TL;DR: A novel uniform-forcing transceiver design is proposed for over-the-air function computation to compensate the non-uniform fading of different sensors and is able to achieve significant performance gain with low complexity.
Abstract: The future Internet-of-Things network is expected to connect billions of sensors, which incurs high latency for data aggregation. To overcome this challenge, a new technique called over-the-air function computation was recently developed to enable fusion center to receive a desired function directly. It utilizes the superposition property of wireless channel to realize the uniform summation of the desired function. In order to compensate the non-uniform fading of different sensors, we propose a novel uniform-forcing transceiver design for over-the-air function computation. A corresponding min-max optimization problem is formulated to minimize the distortion of the computation which is measured by mean squared error. Due to the non-convexity of the problem, it is relaxed to semidefinite programming first. Then, the performance of the initial solution is improved through successive convex approximation. Simulation results show that the proposed design is able to achieve significant performance gain with low complexity.
TL;DR: The proposed D2D aided CRS using non-orthogonal multiple access (NOMA) with the power allocation is shown to improve the achievable rate greatly compared to conventional CRSs with and without NOMA, and it is proved that the sum-capacity scaling is log SNR for the proposed one, whereas (2/3) log SNr for the conventional ones.
Abstract: This letter proposes a device-to-device (D2D) aided cooperative relaying system (CRS) using non-orthogonal multiple access (NOMA) to enhance the spectral efficiency. In addition, a power allocation strategy is proposed to achieve the maximum capacity scaling according to signal-to-noise ratio (SNR). The proposed D2D aided CRS using NOMA with the power allocation is shown to improve the achievable rate greatly compared to conventional CRSs with and without NOMA. In particular, it is proved that the sum-capacity scaling is log SNR for the proposed one, whereas (2/3) log SNR for the conventional ones.
TL;DR: The obtained results reveal the importance of taking the eavesdropper location uncertainty into consideration while designing V2V communication systems.
Abstract: In this letter, we study the physical layer secrecy performance of the classic Wyner’s wiretap model over double Rayleigh fading channels for vehicular communications links. We derive novel and closed-form expressions for the average secrecy capacity (ASC) taking into account the effects of fading, path loss, and eavesdropper location uncertainty. The asymptotic analysis for ASC is also conducted. The derived expressions can be used for secrecy capacity analysis of a number of scenarios including vehicular-to-vehicular (V2V) communications. The obtained results reveal the importance of taking the eavesdropper location uncertainty into consideration while designing V2V communication systems.
TL;DR: This letter investigates the outage probability (OP) of amplify-and-forward hybrid satellite-terrestrial relay networks with a nonorthogonal multiple access (NOMA) scheme and derives closed-form OP expressions for each NOMA user.
Abstract: In this letter, we investigate the outage probability (OP) of amplify-and-forward hybrid satellite-terrestrial relay networks with a nonorthogonal multiple access (NOMA) scheme. By assuming that a single antenna satellite communicates with multiple multiantenna users simultaneously through the help of a single antenna relay and the NOMA scheme, we first derive the closed-form OP expressions for each NOMA user. Then, asymptotic OP expressions at the high signal-to-noise ratio regime are also obtained to evaluate the achievable diversity order and coding gain. Finally, simulations are provided to the validity of theoretical results, the superiority of introducing the NOMA scheme in satellite-terrestrial relay networks, and the effect of key parameters on the performance of NOMA users.
TL;DR: The superiority of UP-NOMA over conventional multiple access is demonstrated through simulation and analysis.
Abstract: A coordinated direct and relay transmission is proposed using uplink non-orthogonal multiple access (UP-NOMA). A two-user UP-NOMA scenario is considered, where a cell-center user directly communicates with a base station (BS), whereas a cell-edge user needs the assistance of a half-duplex decode-and-forward relay to communicate with the BS. The ergodic sum capacity of UP-NOMA is analyzed under both perfect and imperfect successive interference cancellation. The superiority of UP-NOMA over conventional multiple access is demonstrated through simulation and analysis.
TL;DR: This letter introduces a novel joint channel estimation (CE) and multiuser detection (MUD) framework for the frame based multi-user transmission scenario where users are (in)active for the duration of a frame.
Abstract: Grant-free non-orthogonal multiple access is an emerging research topic in machine-type communications, which is used to reduce signaling overhead. In this context, this letter introduces a novel joint channel estimation (CE) and multiuser detection (MUD) framework for the frame based multi-user transmission scenario where users are (in)active for the duration of a frame. First, considering the inherent frame-wise joint sparsity of the pilot and data phases in the entire frame, we formulate the multiple measurement vector-compressive sensing (MMV-CS) framework. Then, transfer the MMV-CS to a block-sparse single measurement vector-CS (BS-SMV-CS) model. Finally, to make explicit use of the block sparsity inherent in the BS-SMV-CS model and consider that the user sparsity level should be unknown for receiver, an enhanced subspace pursuit (SP) algorithm is developed, i.e., block sparsity adaptive SP. Superior performance of the proposed joint CE and MUD framework is demonstrated by simulation results.
TL;DR: Two machine learning-based schemes are proposed, namely, the support vector machine- based scheme and the naive-Bayes-based scheme, to select the optimal antenna that maximizes the secrecy performance of the considered system.
Abstract: In this letter, we exploit the potential benefits of machine learning in enhancing physical layer security in multi-input multi-output multi-antenna-eavesdropper wiretap channels. To this end, we focus on the scenario where the source adopts transmit antenna selection (TAS) as the transmission strategy. We assume that the channel state information (CSI) of the legitimate receiver is available to the source, while the CSI of the eavesdropper can be either known or not known at the source. By modeling the problem of TAS as a multiclass classification problem, we propose two machine learning-based schemes, namely, the support vector machine-based scheme and the naive-Bayes-based scheme, to select the optimal antenna that maximizes the secrecy performance of the considered system. Compared to the conventional TAS scheme, we show that our proposed schemes can achieve almost the same secrecy performance with relatively small feedback overhead. The work presented here provides insights into the design of new machine learning-based secure transmission schemes.
TL;DR: Simulation results validate the superiority of proposed resource allocation algorithm over the existing orthogonal multiple access scheme and propose a dual-based iterative algorithm to solve the resource allocation problem.
Abstract: This letter investigates the power control and channel assignment problem in device-to-device (D2D) communications underlaying a non-orthogonal multiple access (NOMA) cellular network. With the successive interference cancellation decoding order constraints, our target is to maximize the sum rate of D2D pairs while guaranteeing the minimum rate requirements of NOMA-based cellular users. Specifically, the optimal conditions for power control of cellular users on each subchannel are derived first. Then, based on these results, we propose a dual-based iterative algorithm to solve the resource allocation problem. Simulation results validate the superiority of proposed resource allocation algorithm over the existing orthogonal multiple access scheme.
TL;DR: The achievable sum rate of NOMA can be lower than that of OMA even in the regime of low number of users due to intra-cluster pilot contamination and error propagation of imperfect SIC.
Abstract: A cell-free massive multiple-input multiple-output system with non-orthogonal multiple-access (NOMA) is investigated. An achievable sum rate is derived and compared against the orthogonal multiple access (OMA) counterpart. Thereby, the detrimental effects of intra-cluster pilot contamination, inter-cluster interference and imperfect successive interference cancellation (SIC) are investigated. The number of users served simultaneously by NOMA can be significantly higher than that of OMA. Nevertheless, the achievable sum rate of NOMA can be lower than that of OMA even in the regime of low number of users due to intra-cluster pilot contamination and error propagation of imperfect SIC.
TL;DR: This letter designs two algorithms addressing these couplings between the RAN resource allocation for each slice and the coordination of the slices that share the same resources, which is formulated as a bi-convex problem.
Abstract: Network slicing is considered to be a crucial feature of fifth generation cellular systems. By dividing the network infrastructure into multiple logical segments, network slicing can support parallel services with different requirements. While technology developments focus on slicing the core networks, there are limited studies in network slicing for radio access networks (RAN). Hence, in this letter, we study the RAN slicing and slice coordination, which is formulated as a bi-convex problem. Even though there are complicated couplings between the RAN resource allocation for each slice and the coordination of the slices that share the same resources, we design two algorithms addressing these couplings of this bi-convex problem. Simulation results validate the efficacy of our proposed algorithms.
TL;DR: In this letter, secure beamforming designs are investigated in a multiple-input multiple-output secrecy channels with simultaneous wireless information and power transfer and an SCA-based iterative algorithm is proposed in the perfect channel state information (CSI) case to obtain a near-optimal rank-one solution.
Abstract: In this letter, secure beamforming designs are investigated in a multiple-input multiple-output secrecy channels with simultaneous wireless information and power transfer. In order to achieve fairness among different multiple energy harvesting receivers, the minimum harvested energy is maximized under the secrecy rate requirements. In particular, in order to reduce the computational complexity of the semidefinite programming problem, a successive convex approximation (SCA) iterative algorithm is proposed in the perfect channel state information (CSI) case to obtain a near-optimal rank-one solution. Moreover, the original problem is extended to the imperfect CSI case by incorporating a norm-bounded error model, where an SCA-based iterative algorithm is also proposed. Simulation results reveal that the SCA-based iterative algorithm achieves the same performance as the semidefinite relaxation method with reduced complexity.
TL;DR: Numerical results validate that multipair massive MIMO two-way relaying systems are robust to hardware impairments at the relay.
Abstract: We consider a multipair massive multiple-input multiple-output (MIMO) two-way relaying system, where multiple pairs of single-antenna devices exchange data with the help of a relay employing a large number of antennas ${N}$ . The relay consists of low-cost components that suffer from hardware impairments. A large-scale approximation of the spectral efficiency with maximum ratio processing is derived in closed form, and the approximation is tight as ${N \to \infty }$ . It is revealed that for a fixed hardware quality, the impact of the hardware impairments vanishes asymptotically when ${N}$ grows large. Moreover, the impact of the impairments may even vanish when the hardware quality is gradually decreased with ${N}$ , if a scaling law is satisfied. Finally, numerical results validate that multipair massive MIMO two-way relaying systems are robust to hardware impairments at the relay.
TL;DR: Simulation results validate the effectiveness of the proposed algorithm and reveal that the optimized transmit power shows a water-filling characteristic in spatial domain.
Abstract: This letter investigates the transmit power and trajectory optimization problem for unmanned aerial vehicle (UAV)-aided networks. Different from majority of the existing studies with fixed communication infrastructure, a dynamic scenario is considered where a flying UAV provides wireless services for multiple ground nodes simultaneously. To fully exploit the controllable channel variations provided by the UAV’s mobility, the UAV’s transmit power and trajectory are jointly optimized to maximize the minimum average throughput within a given time length. For the formulated non-convex optimization with power budget and trajectory constraints, this letter presents an efficient joint transmit power and trajectory optimization algorithm. Simulation results validate the effectiveness of the proposed algorithm and reveal that the optimized transmit power shows a water-filling characteristic in spatial domain.
TL;DR: This letter solves the assignment problem using machine learning approach, and the linear sum assignment problems (LSAPs) are solved by the deep neural networks (DNNs).
Abstract: Many resource allocation issues in wireless communications can be modeled as assignment problems and can be solved online with global information. However, traditional methods for assignment problems take a lot of time to find the optimal solutions. In this letter, we solve the assignment problem using machine learning approach. Specifically, the linear sum assignment problems (LSAPs) are solved by the deep neural networks (DNNs). Since LSAP is a combinatorial optimization problem, it is first decomposed into several sub-assignment problems. Each of them is a classification problem and can be solved effectively with DNNs. Two kinds of DNNs, feed-forward neural network and convolutional neural network, are implemented to deal with the sub-assignment problems, respectively. Based on computer simulation, DNNs can effectively solve LSAPs with great time efficiency and only slight loss of accuracy.
TL;DR: This letter considers a practical downlink NOMA system in the Nakagami- ${m}$ fading channels, of which the transmitter only knows the statistical CSI associated with each user, and derives a closed-form formulation of each user’s outage probability.
Abstract: Conventional non-orthogonal multiple access (NOMA) schemes assume perfect channel state information (CSI) at the transmitter side, which is nearly impractical for many communication scenarios. Instead, this letter considers a practical downlink NOMA system in the Nakagami- ${m}$ fading channels, of which the transmitter only knows the statistical CSI associated with each user. We analyze the outage performance of the proposed system and derive a closed-form formulation of each user’s outage probability. Moreover, based on statistical CSI, the transmitter can optimize the system’s sum throughput over power allocation for different users. The accuracy of our outage analysis and the efficacy of the proposed optimization algorithm are both confirmed by simulation results.
TL;DR: In this paper, a novel localization scenario based on received signal strength from terrestrial nodes is introduced, which includes height-dependent path loss exponent and shadowing which results in an optimum UAV altitude for minimum localization error.
Abstract: In this letter, the localization of terrestrial nodes when unmanned aerial vehicles (UAVs) are used as base stations is investigated. Particularly, a novel localization scenario based on received signal strength from terrestrial nodes is introduced. In contrast to the existing literature, our analysis includes height-dependent path loss exponent and shadowing which results in an optimum UAV altitude for minimum localization error. Furthermore, the Cramer-Rao lower bound is derived for the estimated distance which emphasizes, analytically, the existence of an optimal UAV altitude. Our simulation results show that the localization error is decreased from over 300 m when using ground-based anchors to 80 m when using UAVs flying at the optimal altitude in an urban scenario.