With the concept of low-altitude economy (LAE) being released recently, the research on ultra-low latency communication and rich computation capacity technologies to support LAE-enabled internet of things (IoT) networks has attracted interest from industry and academia. One of the key challenges is to reduce the latency of the IoT networks while guaranteeing the quality of service among all user devices (UDs). In this paper, we propose an LAE-enabled IoT network, where a UAV-carried mobile edge computing (MEC) server offers extra computation capacity to all UDs to process their computational tasks remotely. To minimise the total service delay of all UDs, which consists of the transmission delay, processing delay, queueing delay, and UAV mobility delay, we propose a deep-Q-leaning (DQN)-based optimisation algorithm by jointly optimising the task offloading decisions and communication and computation resource allocation for all the UDs in the LAE-enabled IoT network. Simulation results illustrate that our proposed algorithm achieves a much lower total service delay than the benchmarks.

This correspondence studies intelligent reflecting surface (IRS) backscatter enabled uplink multi-user communications coexisting with radar sensing. In the system, the radar signal for target detection is harnessed by multiple users, each connected to one IRS that can send information to the base station through backscatter communications in a mode of time division multiple access. Our goal is to maximize the weighted sum bits per unit bandwidth for the uplink multi-user communications by jointly optimizing the transmitting signal at the radar, the passive beamforming at the IRSs, the time allocation among all the users, and the receiving beamforming at the radar, while guaranteeing the echo reception at the radar. To tackle the non-convex optimization problem, a method of alternative optimization as well as fractional programming is developed. The simulation results validate the feasibility of the considered system and show the communication performance with sensing.

The next generation of communication systems are expected to operate at high frequency bands such as millimetre wave (mmWave) and terahertz (THz) bands, and use extremely large-scale multiple-input-multiple-output (XL-MIMO). This brings a paradigm shift from far-field to near-field communications. In this paper, we investigate physical-layer key generation in near-field XL-MIMO communications and focus on the most challenging line-of-sight (LoS) propagation scenario. To be specific, we introduce artificial randomness to enhance secret key generation and enable theoretical analysis of secret key rate (SKR). We provide the zero-forcing (ZF) precoding solution that can null the received signal at the eavesdropper. We show that the ZF precoding leads to a low SKR in challenging scenarios of low transmit powers and small eavesdropping distances. To improve the SKR in these challenging scenarios, we propose a novel low-complexity machine learning-based beam focusing (MLBF) scheme. Simulation results show that the proposed MLBF scheme achieves a higher SKR than the benchmark methods.

Over-the-air computation (AirComp) is considered as a communication-efficient solution for data aggregation and distributed learning by exploiting the superposition properties of wireless multi-access channels. However, AirComp is significantly affected by the uneven signal attenuation experienced by different wireless devices. Recently, Cell-free Massive MIMO (mMIMO) has emerged as a promising technology to provide uniform coverage and high rates by joint coherent transmission. In this paper, we investigate AirComp in Cell-free mMIMO systems, taking into account spatially correlated fading and channel estimation errors. In particular, we propose optimal designs of transmit coefficients and receive combing at different levels of cooperation among access points. Numerical results demonstrate that Cell-free mMIMO using fully centralized processing significantly outperforms conventional Cellular mMIMO with regard to the mean squared error (MSE). Moreover, we show that Cell-free mMIMO using local processing and large-scale fading decoding can achieve a lower MSE than Cellular mMIMO when the wireless devices have limited power budgets.

Physical layer key generation (PLKG) leverages wireless channels to produce secret keys for legitimate users. However, in millimetre-wave (mmWave) frequency bands, the presence of blockage significantly reduces the key rate (KR) of a PLKG system. To address this issue, we introduce reconfigurable intelligent surfaces (RISs) as a potential solution for constructing RIS-reflected channels, thereby enhancing the KR. Our study focuses on the beam-domain channel model and exploits the sparsity of mmWave bands to enhance the randomness of secret keys. To relieve pilot overhead in multi-user systems, we employ a compressed sensing (CS) algorithm to estimate angular information and propose a channel probing protocol with the full-array configuration for acquiring the beam-domain channel. We derive the analytical expressions for the KR in the case of full-array configuration. To optimize the KR, we design the phase shift and precoding vectors based on the obtained angular information. Furthermore, we employ a water-filling algorithm that relies on the Karush-Kuhn-Tucker (KKT) conditions to optimize power allocation for estimating the beam-domain channel with the same channel variance. When channel variances of the beam-domain channel differ, we design a deep-learning-based power allocation method for a more complex problem. What is more, we design a sub-array configuration scheme that exploits the difference in spatial angles between users to reduce pilot overhead and derive the analytical expression for the KR. Through extensive simulations, we demonstrate that our proposed PLKG schemes outperform existing methods.

In contrast to the intelligent reflecting surface (IRS) as a pure reflection device in the past, in this letter, from a more micro perspective, we propose to employ pseudo-random constellation rotation to secure microwave quick response (QR) code communication. Specifically, confidential information is encoded into a QR code, which is displayed on an IRS. Pseudo-random constellation rotation is applied to each element of the IRS to encrypt the QR code. This ensures that only the authenticated user (Bob) can decode and access the confidential information, while an eavesdropper (Eve) is unable to correctly interpret the QR code. We derive a closed-form expression for the bit error rate (BER) and analyze the impact of various factors on the BER for both Bob and Eve, including the distance between Bob and the IRS, Bob's transmit power, and the Rician factor. Simulation results demonstrate the proposed scheme's effectiveness in achieving secure communication.