The lottery ticket hypothesis (LTH) claims that a deep neural network (i.e., ground network) contains a number of subnetworks (i.e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network. F
ederated learning (FL) has recently been applied in LotteryFL to discover such winning tickets in a distributed way, showing higher accuracy multi-task learning than Vanilla FL. Nonetheless, LotteryFL relies on unicast transmission on the downlink, and ignores mitigating stragglers, questioning scalability. Motivated by this, in this article we propose a personalized and communication-efficient federated lottery ticket learning algorithm, coined CELL, which exploits downlink broadcast for communication efficiency. Furthermore, it utilizes a novel user grouping method, thereby alternating between FL and lottery learning to mitigate stragglers. Numerical simulations validate that CELL achieves up to 3.6% higher personalized task classification accuracy with 4.3x smaller total communication cost until convergence under the CIFAR-10 dataset.
Multi-point vehicular positioning is one essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight. Besid
es, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this paper, we aim at overcoming these limitations by proposing a novel relative positioning approach, called Cooperative Multi-point Positioning (COMPOP). The COMPOP establishes cooperation between a target vehicle (TV) and a sensing vehicle (SV) if a LoS path exists, where a TV explicitly lets an SV to know the TVs existence by transmitting positioning waveforms. This cooperation makes it possible to remove the time-consuming scanning and target recognizing processes, facilitating real-time positioning. One prerequisite for the cooperation is a clock synchronization between a pair of TV and SV. To this end, we use a phase-differential-of-arrival based approach to remove the TV-SV clock difference from the received signal. With clock difference correction, the TVs position can be obtained via peak detection over a 3D power spectrum constructed by a Fourier transform (FT) based algorithm. The COMPOP also incorporates nearby vehicles, without knowing their locations, into the above cooperation for the case without a LoS path. The effectiveness of the COMPOP is verified by several simulations concerning practical channel parameters.
This letter proposes a novel random medium access control (MAC) based on a transmission opportunity prediction, which can be measured in a form of a conditional success probability given transmitter-side interference. A transmission probability depen
ds on the opportunity prediction, preventing indiscriminate transmissions and reducing excessive interference causing collisions. Using stochastic geometry, we derive a fixed-point equation to provide the optimal transmission probability maximizing a proportionally fair throughput. Its approximated solution is given in closed form. The proposed MAC is applicable to full-duplex networks, leading to significant throughput improvement by allowing more nodes to transmit.
Vehicle-to-Everything (V2X) will create many new opportunities in the area of wireless communications, while its feasibility on enabling vehicular positioning has not been explored yet. Vehicular positioning is a crucial operation for autonomous driv
ing. Its complexity and stringent safety requirement render conventional technologies like RADAR and LIDAR inadequate. This article aims at investigating whether V2X can help vehicular positioning from different perspectives. We first explain V2Xs critical advantages over other approaches and suggest new scenarios of V2X-based vehicular positioning. Then we review the state-of-the-art positioning techniques discussed in the ongoing 3GPP standardization and point out their limitations. Lastly, some promising research directions for V2X-based vehicular positioning are presented, which shed light on realizing fully autonomous driving by overcoming the current barriers.
Autonomous driving (auto-driving) has been becoming a killer technology for next generation vehicles, whereas some fatal accidents grow concerns about its safety. A fundamental function for safer auto-driving is to recognize the vehicles locations, t
ermed vehicular positioning. The state-of-the-art vehicular positioning is to rely on anchors that are stationary objects whose locations are known, i.e. satellites for GPS and base stations for cellular positioning. It is important for reliable positioning to install anchors densely, helping find enough anchors nearby. For the deployment to be cost-effective, there are some trials to use backscatter tags as alternative anchors by deploying them on a road surface, but its gain is limited by several reasons such as short contact time and difficulties in maintenance. Instead, we propose a new backscatter-tag assisted vehicular positioning system where tags are deployed along a roadside, which enables the extension of contact duration and facilitates the maintenance. On the other hand, there is a location mismatch between the vehicle and the tag, calling for developing a new backscatter transmission to estimate their relative position. To this end, we design a novel waveform called joint frequency-and-phase modulation (JFPM) for backscatter-tag assisted vehicular positioning where a transmit frequency is modulated for the distance estimation assuming that the relevant signal is clearly differentiable from the others while the phase modulation helps the differentiation. The JFPM waveform leads to exploiting the maximum Degree-of-Freedoms (DoFs) of backscatter channel in which multiple-access and broadcasting channels coexist, leading to more accurate positioning verified by extensive simulations.
Multi-point detection of the full-scale environment is an important issue in autonomous driving. The state-of-the-art positioning technologies (such as RADAR and LIDAR) are incapable of real-time detection without line-of-sight. To address this issue
, this paper presents a novel multi-point vehicular positioning technology via emph{millimeter-wave} (mmWave) transmission that exploits multi-path reflection from a emph{target vehicle} (TV) to a emph{sensing vehicle} (SV), which enables the SV to fast capture both the shape and location information of the TV in emph{non-line-of-sight} (NLoS) under the assistance of multi-path reflections. A emph{phase-difference-of-arrival} (PDoA) based hyperbolic positioning algorithm is designed to achieve the synchronization between the TV and SV. The emph{stepped-frequency-continuous-wave} (SFCW) is utilized as signals for multi-point detection of the TVs. Transceiver separation enables our approach to work in NLoS conditions and achieve much lower latency compared with conventional positioning techniques.
Accurate vehicular sensing is a basic and important operation in autonomous driving. Unfortunately, the existing techniques have their own limitations. For instance, the communication-based approach (e.g., transmission of GPS information) has high la
tency and low reliability while the reflection-based approach (e.g., RADAR) is incapable of detecting hidden vehicles (HVs) without line-of-sight. This is arguably the reason behind some recent fatal accidents involving autonomous vehicles. To address this issue, this paper presents a novel HV-sensing technology that exploits multi-path transmission from a HV to a sensing vehicle (SV). The powerful technology enables the SV to detect multiple HV-state parameters including position, orientation of driving direction, and size. Its implementation does not even require transmitter-receiver synchronization like conventional mobile positioning techniques. Our design approach leverages estimated information on multi-path (AoA/AoD/ToA) and their geometric relations. As a result, a complex system of equations or optimization problems, where the desired HV-state parameters are variables, can be formulated for different channel-noise conditions. The development of intelligent solution methods ranging from least-square estimator to disk/box minimization yields a set of practical HV-sensing techniques. We study their feasibility conditions in terms of the required number of paths. Furthermore, practical solutions, including sequential path combining and random directional beamforming, are proposed to enable HV-sensing given insufficient paths. Last, realistic simulation of driving in both highway and rural scenarios demonstrates the effectiveness of the proposed techniques. In summary, the proposed technique will enhance the capabilities of existing vehicular sensing technologies by enabling HV-sensing.
