This paper studies time difference of arrival (TDoA)based algorithms for drone controller localization and analyzes TDoA estimation in multipath channels. Building on TDoA estimation, we propose two algorithms to enhance localization accuracy in multipath environments: the Maximum Likelihood (ML) algorithm, and the Least Squares Bancroft with GaussNewton (LS-BF-GN) algorithm. We evaluate these proposed algorithms in two typical outdoor channels: Wireless Local Area Network (WLAN) Channel F and the two-ray ground reflection (TRGR) channel. Our simulation results demonstrate that the ML and LS-BF-GN algorithms significantly outperform the LSBF algorithm in multipath channels. To further enhance localization accuracy, we propose averaging multiple tentative location estimations. Additionally, we evaluate the impact of time synchronization errors among sensors on localization performance through simulation.

Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) are two important wireless communication traffics to build a digital twin of physical reality. Therein, eMBB and URLLC traffics are to transmit high-quality sensed data and critical commands, respectively. To support these two important traffics, we develop an intelligent resource allocation mechanism. First, we model the time-frequency resource allocation as an optimization problem aiming to maximize the throughput for the eMBB traffics according to their urgency subject to the constraint on the successful transmission for the URLLC traffics. In this way, the amount of resources allocated to each traffic can be appropriately determined without causing waste in resource usage. Secondly, we propose a feasible low-complexity solution for the optimization problem by relaxing it and then applying linear programming. Thirdly, to address the possible failure of the algorithm due to the relaxation, we propose a post-processing by puncturing the resource initially allocated to eMBB traffics and thereafter reallocating this resource to URLLC traffics. By such, the characteristic of the eMBB traffic, i.e., high throughput, and that of the URLLC traffic, i.e., low latency and ultra reliability, can be achieved. We perform system-level simulations on Matlab 5 G simulation platform to evaluate the performance of the proposed mechanism under different scenarios. Simulations show that the proposed mechanism achieves better performance compared to existing schemes regarding the total eMBB throughput and URLLC failure probability on all the scenarios.

We propose a scheme which generates shared secret keys from the amplitude of frequency domain channel state information (CSI) of individual orthogonal frequency-division multiplexing (OFDM) subcarrier. A unique quantization scheme is proposed, with the use of two parameters,$a$and β, to set the quantization threshold. Based on multivariate Rayleigh distribution, we derive the analytical expressions for key bit disagreement rate (BDR) and key length. The theoretical analysis of BDR and key length aligns with the simulation results. We also present a mathematical model to optimize the parameters of our proposed quantization scheme and a guideline to set$a$and β. Our proposed method for iterative tuning of β can balance the number of zeros and ones in the generated key sequences so that the randomness requirement is met.

We propose a bistatic joint radar and communication (JRC) based on standardized 5G new radio (NR) waveform, where the receiver can decode the transmitted signal, and estimate the range, speed, and angle of the radar targets at the same time. The proposed solution consists of several key components, including a) a decision feedback architecture and two JRC algorithms for bistatic radar sensing; b) a multi-user bistatic JRC solution; and c) a direction of arrival (DOA) estimation algorithm based on the range-Doppler map (RDM). We present simulation results to demonstrate the effectiveness of our proposed solution, and its ability to work well in the multi-user scenarios where multiple users share the same spectrum, making it a promising solution for practical applications that require radar sensing in crowded environments.

For massive machine type communications (mMTC), it is critical to squeeze the transmission overhead as packet length is usually short and power is limited. Reducing preamble/pilot length for channel estimation is thus very important. In this paper, we propose to use short preamble for channel estimation in generalized frequency division multiplexing (GFDM) communication. Based on the short preamble, we can estimate a short channel first. We then show that the conventional zero-padding method for extending short channel to long channel in GFDM is not accurate. A new efficient method to construct the long effective channel from the obtained short channel is proposed. The proposed new method can construct a long channel for GFDM equalization without knowing the time sync error. It is proved theoretically that the constructed channel is correct given that the length of cyclic prefix (CP) and cyclic suffix (CS) are longer than the original channel length plus the time sync error. Simulations at various situations are shown to verify the results.

In this paper, we propose a novel scheme called subband random sensing (SRS) grant free uplink for ultra-reliable low-latency communication (URLLC) in unlicensed spectrum. The SRS grant free uplink creatively combines the subband sensing, user grouping and random access, which allows a user sensing the unlicensed spectrum to use available sub-resources in a wideband and reduce collisions with other users. The new scheme overcomes the severe spectrum wastage problem of the semi-persistent scheduling (SPS) uplink adopted by new radio (NR) release 16 in applications with sporadic traffic. Compared with the contention based grant free uplink, the new scheme achieves much lower collision probability, which directly leads to higher reliability. Analysis and simulations are provided to prove the exceptional performances.

We evaluate the performance of machine learning method in identifying the channel type in 802.11ac systems. It is shown that the reference symbols contained in the packet preamble can be used as a good feature for classification. In addition, the time-domain received preamble also serves as a good classification feature with comparable performance, allowing early identification of channel type since the information can be tapped closer to the receiving antenna. We validate our approach in both software and hardware simulation, and classification accuracy of more than 94% is shown to be attainable at moderate Signal to Noise Ratio (SNR). Finally, we also evaluate the application of our proposed algorithm into link adaptation in 802.11ac systems, and show that up to 1.6dB gain can be achieved compared to the case without channel type identification.

In IIoT application, various IoT devices with different protocols are connected. One of the major challenges in IIoT application is to detect interference and achieve coexistence across multiple technologies. In this paper, we propose a novel and efficient method to detect the presence of Microwave Oven (MWO) interference. In order to achieve coexistence between IEEE 802.11 WIFI and IEEE 802.15.4 based sensor network, we propose an enhanced spectral matching method to detect 802.15.4 packet and WIFI packet. Our proposed method can also detect the hopping frequency of 802.15.4 packet. Simulation results show that our proposed method can achieve high detection probability in various propagation channels.