LPD-Aware Uplink CSI-based 5G NR Downlink Synchronization for Tactical Networks
5G NR is touted to be an attractive candidate for tactical networks owing to its versatility, scalability, and low cost. However, tactical networks need to be stealthy, where an adversary is not able to detect or intercept the tactical communication. In this paper, we investigate the stealthiness of 5G NR by looking at the probability with which an adversary that monitors the downlink synchronization signals can detect the presence of the network. We simulate a single-cell single-eavesdropper scenario and evaluate the probability with which the eavesdropper can detect the synchronization signal block when using either a correlator or an energy detector. We show that this probability is close to $ 100\% $ suggesting that 5G out-of-the-box is not suitable for a tactical network. We then propose utilizing the uplink channel-state-information to beamform the downlink synchronization-signals towards the tactical user-equipment (UE) to lower the eavesdropper detection probability while not compromising the performance of the legitimate tactical UE.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction
Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss predictions can optimize use of the radio frequenc…
Data AugmentationMix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD. To address uplink-downlink capacity asymmetry, local model outputs are uploaded to a ser…
Federated LearningPrivacy PreservingMulti-Target Two-way Integrated Sensing and Communications with Full Duplex MIMO Radios
In this paper, we propose a multiple input multiple output (MIMO) Full-Duplex Integrated Sensing and Communication System consisting of multiple targets, a single downlink, and a single uplink user. We employed signal-to…
Integrated sensing and communicationparameter estimationConvergence of Federated Learning over a Noisy Downlink
We study federated learning (FL), where power-limited wireless devices utilize their local datasets to collaboratively train a global model with the help of a remote parameter server (PS). The PS has access to the global…
Federated LearningQuantizationFedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
Federated learning (FL) is a powerful machine learning paradigm which leverages the data as well as the computational resources of clients, while protecting clients' data privacy. However, the substantial model size and …
Federated LearningQuantization