In this paper, an integrated sensing and communication (ISAC) system was proposed under cell-free architecture with different power allocation schemes for both communication and sensing functions. In addition to using uplink (UL) pilot transmission for channel estimation, a group of pre-designed orthogonal downlink (DL) pilot sequences are inserted and transmitted from each cooperative access point (AP) towards the user equipments (UEs). The inserted DL pilots in cell-free ISAC are utilized for both channel estimation in communication and cooperative localization in radar sensing. Based on the ISAC strategy, a power allocation scheme is proposed with the objective of minimizing the maximum channel estimation error for each user, under the power constraints of APs and minimum mean square error constraints of the localization. The full-power scheme which APs consume all the available power during the DL phase is also studied and analyzed. The performance of the proposed ISAC strategy is evaluated under a realistic highway scenario, and compared to the existing non-DL strategies. Additionally, we investigate the impact of the pilot ratio under a constrained signal length, as well as the impact of the pilot proportion that the pilot length to the entire signal frame length. Simulation results show that the performances of both channel estimation and localization can be significantly improved at a relatively low cost of extra power needed for APs. Moreover, the analysis provides insights into optimizing the DL/UL pilot ratio and pilot proportion which can significantly enhance both channel estimation and localization accuracy, while ensuring sufficient resources remain available for data payload.

As an evolution of cloud radio access network (C-RAN), fog radio access network (F-RAN) becomes promising for future mobile communications by enabling processing and caching at fog access points (FAPs). Different from the centralised C-RAN, F-RAN has a semi-distributed architecture, aiming to alleviate traffic load on the fronthaul links in C-RAN. Under the semi-distributed architecture in F-RAN, which employs a cell-free multiple input multiple output (MIMO) access technique, decisions on the joint user-FAP association and transmit power allocation are made at individual FAPs. To mitigate strong interference, FAPs will need to exchange cooperative status information, such as CSI, user association details or transmission power levels. However, this can lead to significant communication overhead within the network and introduce high complexity in the decision-making process. In this paper, accounting for the semi-distributed nature of the F-RAN architecture, reinforcement learning is leveraged as a potential solution to this kind of problem, and a novel multi-agent dual deep Q-network (MA-DDQN) algorithm is proposed by introducing experience exchange in partially observable Markov decision process environments. The simulation results show that the proposed reinforcement learning based algorithm outperforms the DDQN algorithm as well as the existing low-complexity algorithms.

In this paper, downlink (DL) pilot based integrated sensing and communication (ISAC) is explored in cell-free networks. To improve both accuracy of channel estimation and localization in the ISAC based cell free network, a group of pre-designed DL orthogonal pilot sequences are introduced in the proposed ISAC strategy for both channel estimation for communications and cooperative sensing with radars. A power allocation scheme is proposed with the objective of minimizing the maximum channel estimation error for each user, under the power constraints of APs and minimum mean square error of sensing based localization. The performance of the proposed ISAC strategy is evaluated under a realistic highway scenario, and compared to the conventional methods. The results show that the performances of both channel estimation and localization can be significantly improved at a relatively low cost of extra power needed for APs near users.

Mobile edge computing (MEC) and vehicle-to-everything (V2X) communications are two promising technics to support delay-sensitive applications for autonomous driving. In this paper, road side unit (RSU) and assistant vehicle offloading are jointly considered to minimize latency for vehicular tasks in an MEC-V2X network. The impact of Doppler spread caused by high moving speed is also considered. The offloading decision, transmit power, bandwidth, and computation resource allocations are solved by formulating them as a joint optimization problem. The simulation results show that the proposed scheme significantly reduced the task latency, compared to three traditional methods, where task either run locally, or at MEC server, or adaptively between MEC server and local.

This letter proposes a nonparametric detector, termed as Gini Correlation (GC), to solve the classical problem of detecting deterministic signals buried in impulsive noise. With the help of the popular Middleton’s Class-A impulsive noise (MCAN) model, we derive the expectation and variance of GC under alternative hypothesis and null hypothesis, which, along with the central limit theorem, are further employed for determining the detection probability and the detection threshold. The results show that the proposed detector possesses a constant false alarm rate property. Monte Carlo simulations verify not only the correctness of our theoretical findings but also the superiority of GC to other state-of-the-art methods in terms of receiver operating characteristic (ROC) curves and asymptotic relative efficiency (ARE) curves.

This letter proposes to apply two rank correlations, namely, Kendalls tau (KT) and Spearmans rho (SR), to the fundamental problem of detecting known signals in impulsive noise. Under the popular Middletons Class-A impulsive noise model, we derived the expectations and variances of KT and SR, which, along with the central limit theorem, are further employed to determining the detection threshold and the detection probability. Monte Carlo simulations not only verified the correctness of our theoretical findings but also demonstrated the superiority of KT and SR to the other five state-of-the-art methods in terms of detection probability.