In this article, we exploit the positioning results for communication, and propose an integrated positioning and communication (IPAC) framework for terahertz (THz) massive multi-input-multioutput (MIMO) networks. Specifically, we derive an explicit expression for the Cramér-Rao bound (CRB), which is used to evaluate the positioning performance. Furthermore, based on the established relationship between positioning and communication, we propose a joint beam tracking and power allocation scheme for mobile users in THz massive MIMO networks, which minimizes the positioning error under both the data transmission outage constraint and total power constraints. Unfortunately, the joint beam tracking and power allocation optimization problem is nonconvex, and intractable due to the outage constraint. To address this challenge, we decompose the nonconvex problem into a beam tracking subproblem and a power allocation subproblem, and propose a proximal policy optimization beam tracking (PPO-BT) algorithm for the beam tracking subproblem and a robust power allocation (RPA) algorithm for the power allocation subproblem. Furthermore, we extend the proposed THz IPAC scheme to more practical 3-D scenarios. Simulation results demonstrate that our proposed THz IPAC framework can satisfy positioning and communication requirements at the same time, and our proposed methods outperform existing methods.

Light fidelity (LiFi) is a potential key technology for future 6G networks. However, its feasibility of supporting mobile communications has not been fundamentally discussed. In this paper, we investigate the time-varying channel characteristics of mobile LiFi based on measured mobile phone rotation and movement data. Specifically, we define LiFi channel coherence time to evaluate the correlation of the channel timing sequence. Then, we derive the expression of LiFi transmission rate based on the m-pulse-amplitude-modulation (M-PAM). The derived rate expression indicates that mobile LiFi communications is feasible by using at least two photodiodes (PDs) with different orientations. Further, we propose two channel estimation schemes, and propose a LiFi channel tracking scheme to improve the communication performance. Finally, our experimental results show that the channel coherence time is on the order of tens of milliseconds, which indicates a relatively stable channel. In addition, based on the measured data, better communication performance can be realized in the multiple-input multiple-output (MIMO) scenario with a rate of 36Mbit/s, compared to other scenarios. The results also show that the proposed channel estimation and tracking schemes are effective in designing mobile LiFi systems.

In this paper, we investigate the trade-off between positioning and communication for an integrated positioning and communication (IPAC) millimeter wave system. First, in terms of the positioning in the IPAC system, the Cramér-Rao bound (CRB) is commonly used as a performance metric. Unfortunately, the CRB is only tight in a certain region for the high signal-to-noise ratio (SNR). To compensate for this deficiency, we derive the Ziv-Zakai bound (ZZB) for the IPAC system by exploiting the a priori delay information extracted from both the time delay parameter and the channel amplitude attenuation. Further, we derive the expected CRB (ECRB) and the weighted CRB (WCRB) of the system for comparisons. Second, we analyze the trade-off between positioning and communication for this IPAC system based on the derived ZZB. Specifically, we aim to optimize the power allocation to maximize the achievable data rate subject to the ZZB and total transmit power constraints. Numerical results show that the ZZB provides a tighter and more reasonable bound for the minimum mean square error (MMSE) estimator over the wide range of SNRs compared to the ECRB and WCRB. Moreover, the trade-off between positioning and communication is revealed via changing critical parameters by simulations.

In this article, we consider covert beamforming design for intelligent reflecting surface (IRS)-assisted Internet-of-Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie’s channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the latter case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of the Kullback–Leibler divergence to develop the robust beamformer based on a relaxation,$S$-Lemma, and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.