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.

It is expected that 5G/6G networks will exploit sub-6 GHz, millimetre wave (mmWave) and terahertz (THz) frequency bands simultaneously and will increase flexibility in user equipment (UE)-cell association. In this paper, we introduce a novel stochastic geometry-based framework for the analysis of the signal-to-interference-plus-noise-ratio (SINR) and rate coverage in a multi-tier hybrid sub-6GHz-mmWave-THz network, where each tier has a particular base station (BS) density, transmit power, bandwidth, number of BS antennas, and cell-association bias factor. The proposed framework incorporates the effects of sub-6 GHz, mmWave and THz channel characteristics, BS beamforming gain, and blockages. We investigate the downlink (DL) and uplink (UL) decoupled cell-association strategy and characterise the per-tier cell-association probability. Based on that, we analytically derive the SINR and rate coverage probabilities for both DL and UL transmissions. The analytical results are validated via extensive Monte Carlo simulations. Numerical results demonstrate the superiority of the DL and UL decoupled cell-association strategy in terms of SINR and rate coverage over its coupled counterpart.

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.

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximize the SKR, a joint design problem of the BS’s precoding matrix and the IRS’s phase shift vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimization to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

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 cell-free multiple input multiple output (MIMO) networks, multiple base stations (BSs) collaborate to achieve high spectral efficiency. Nevertheless, high penetration loss due to large blockages in harsh propagation environments is often an issue that severely degrades communication performance. Considering that intelligent reflecting surface (IRS) is capable of constructing digitally controllable reflection links in a low-cost manner, we investigate an IRS-enhanced downlink cell-free MIMO network in this paper. We aim to maximize the weighted sum rate (WSR) of all the users by jointly optimizing the transmit beamforming at the BSs and the reflection coefficients at the IRS. To address the optimization problem, we propose a fully distributed machine learning algorithm. Different from the conventional iterative optimization algorithms that require a central processing at the central processing unit (CPU) and large amount of channel state information and signaling exchange between the BSs and the CPU, in the proposed algorithm, each BS can locally design its beamforming vectors. Meanwhile, the IRS reflection coefficients are determined by one of the BSs. Simulation results show that the deployment of IRS can significantly boost the WSR and that the proposed algorithm can achieve a high WSR with a low computational complexity.

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS's precoding matrix and the IRS's reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

Physical layer key generation (PLKG) exploits the distributed entropy source of wireless channels to generate secret keys for legitimate users. When the millimeter wave (mmWave) channel is blocked, reconfigurable intelligent surfaces (RISs) have emerged as a prospective approach to constructing reflected channels and improving the secret key rate (SKR). This paper investigates the key generation scheme for the RIS-aided mmWave system. We study the beam domain channel model and exploit the sparsity of mmWave bands to reduce the pilot overhead. We propose a channel probing method to acquire the reciprocal angular information and channel gains. To analyze the SKR, we investigate the channel covariance matrix of beam domain channels. We find that the channel gains of beams are uncorrelated which increases the randomness of secret keys. Considering an eavesdropper, we derive the analytical expressions of SKR when the eavesdropping channel has overlapping clusters with the legitimate channel. Simulations validate that the proposed PLKG scheme outperforms existing schemes.

The performance of indoor small-cell networks (SCNs) is affected by the indoor environment, such as walls, blockages, etc. In this article, we investigate the effect of interior wall attenuation on the performance of an indoor SCN. Specifically, the spatial distribution of interior walls is modeled based on the random shape theory, and the indoor base stations (BSs) are distributed following a homogeneous Poisson point process. The channel model includes the path loss, Rayleigh fading, and wall attenuation. We analytically derive the downlink coverage probability under the strongest received signal user association strategy, which is validated by Monte Carlo simulations for three typical interior wall layouts (i.e., random layout, binary orientation layout, and Manhattan grid). The analytical results show that for a given density of interior walls and signal strength attenuation per wall, there is an optimal BS density that maximizes the coverage probability, and the optimal BS density increases as the wall attenuation increases.

Physical layer key generation (PLKG) is a promising technique to establish symmetric keys between resource-constrained legitimate users. However, PLKG suffers from a low key rate in harsh environments where channel randomness is limited. To address the problem, reconfigurable intelligent surfaces (RISs) are introduced to reshape the channels by controlling massive reflecting elements, which can provide more channel diversity. In this paper, we design a channel probing protocol to fully extract the randomness from the cascaded channel, i.e., the channels through reflecting elements. We derive the analytical expressions of the key rate and design a water-filling algorithm based on the Karush-Kuhn-Tucker (KKT) conditions to find the upper bound. To find the optimal precoding and phase shift matrices, we propose an algorithm based on the Grassmann manifold optimization methods. The system is evaluated in terms of the key rate, bit disagreement rate (BDR) and randomness. Simulation results show that our protocols significantly improve the key rate as compared to existing protocols. Compared to multiple-antennas systems without a RIS, our proposed method achieves an average 9.51 dB performance gain when the side length of an element is 1/4 wavelength and the Rician factor is 0 dB.

This paper proposes an intelligent reflecting surface (IRS) backscatter based uplink coordinated transmission strategy for a radio cellular network, where IRS serves as a transmitter enabling uplink transmission from each user to the associated base station (BS). To be specific, the considered network is made up of multiple cells, each of which consists of one multi-antenna BS and its served users. While one multi-antenna power beacon (PB) is deployed to radiate energy-bearing electromagnetic wave, the radio signal received by each IRS is modulated to send its connective user’s information to the associated BS. Based on such a network framework, this paper aims to maximize the weighted sum rate (WSR) under the constraints of total transmit power and reflecting coefficient by joint optimization of active beamforming at the PB, passive beamforming at the IRSs and uplink user scheduling. To address this challenging problem, fractional programming (FP), alternative optimization and weighted bipartite matching are employed to convert the logarithm objective function into a more tractable form and to handle the optimization variables. The simulation results demonstrate the achievable WSR of the considered network.

The authors regret the errors in[1, eqs. (13), (27), (34), and (35)]. The corrections for these equations are given as follows:

With the dense deployment of small cells, the impact of height difference between base stations (BSs) and user equipment (UE) on the performance of heterogeneous networks (HetNets) becomes significant. The traditional two-dimensional models are no longer sufficient to capture the three-dimensional (3D) features of dense HetNets. On the other hand, deploying multiple antennas on BSs is a promising approach to improve network capacity. In this paper, we propose a 3D model for a$K$-tier HetNet with multi-antenna BSs, where different tiers share the same frequency band but may differ in BS height, BS density, number of antennas per BS, BS transmit power, association bias, and path loss exponent. We analytically derive the per-tier association probability under both the strongest received signal and the closest BS cell-association strategies. Based on that, we derive the expressions for the downlink ergodic rate, area spectral efficiency (ASE) and energy efficiency. The numerical results reveal that in the presence of macrocell BSs, for low to medium small-cell BS (SBS) densities, the closest BS cell-association strategy leads to low ergodic rate, ASE and energy efficiency regardless of the SBS height; while at very high SBS densities, under both cell-association strategies, SBSs should be deployed at the same height as UE to achieve high ergodic rate, ASE and energy efficiency. Moreover, we find that for a given SBS height, there exists an optimal combination of SBS density and number of antennas per SBS that maximizes the system energy efficiency.

Interior wall blockage affects the performance of indoor cellular networks but has not been sufficiently studied. In this paper, by modelling the spatial distribution of interior walls using the random shape theory and incorporating the attenuation caused by wall blockages into the path loss model, we develop a tractable approach to analyse the performance of indoor small cell networks under the influence of interior wall blockages. Then, the proposed analytical approach is applied to study the downlink coverage probability of two user association strategies: the closest line-of-sight (LOS) base station (BS) strategy, and the closest non-line-of-sight (NLOS) BS strategy. The analytical results are validated by Monte Carlo simulations in comparison with those obtained under the existing wall blockage model that assumes impenetrable walls. Our results reveal the importance of considering the wall attenuation for the accurate analysis of indoor cellular networks. The coverage probability is more sensitive to wall attenuation under the closest NLOS BS user association strategy than under the closest LOS BS user association strategy, and that there is an optimal BS density that maximizes the coverage probability under the closest LOS BS user association strategy, but the coverage probability under the closest NLOS BS user association strategy monotonically decreases with the increase of BS density.

Mobile data traffic has been largely generated indoors. However, indoor cellular networks have been studied either on a two-dimensional (2D) plane or as an intractable optimization problem for a multi-story building. In this paper, we develop a tractable three-dimensional small-cell network model for a multi-story building. On each story, the small-cell base stations (BS) are distributed following a 2D homogeneous Poisson point process. We analytically derive the downlink coverage probability, spectral efficiency (SE) and area spectral efficiency for the indoor network as functions of the story height, the penetration loss of the ceiling and the BS density. Our tractable expressions show that a higher penetration loss of the ceiling leads to a higher coverage probability and a higher SE. Meanwhile, with the increase of the story height or the BS density, the downlink coverage probability first decreases and then increases after reaching a minimum value, indicating that certain values of story height and BS density should be avoided for good indoor wireless coverage.

To better characterize indoor small cell networks (SCN), we consider the blockages caused by interior walls and employ the bounded path loss model to derive the expression for energy efficiency (EE) of a downlink massive multiple-input multiple-output (MIMO) SCN. Our EE expression demonstrates that a higher penetration loss of interior walls leads to a higher EE. For the purpose of maximizing EE, we propose a novel genetic algorithm (GA) based scheme to jointly optimize the number of antennas per base station (BS), the number of users per cell, and the transmission power per antenna. Numerical results show that our proposed scheme can achieve almost the identical EE as the optimal greedy search algorithm, while significantly reducing the computational time.

The tremendous popularity of Internet-of-Things (IoT) applications and wireless devices has prompted a massive increase of indoor wireless traffic. To further explore the potential of indoor IoT wireless networks, creating constructive interactions between indoor wireless transmissions and the built environments becomes necessary. The electromagnetic (EM) wave propagation indoors would be affected by the EM and physical properties of the building material, e.g., its relative permittivity and thickness. In this article, we construct a new multipath channel model by characterizing wall reflection (WR) for an indoor Line-of-Sight (LOS) single-user multiple-input-multiple-output (MIMO) system and derive its ergodic capacity in the closed-form. Based on the analytical results, we define the wireless friendliness of building material based on the spatially averaged indoor capacity and propose a scheme for evaluating the wireless friendliness of building materials. Monte Carlo simulations validate our analytical results and manifest the significant impact of the relative permittivity and thickness of building material on indoor capacity, indicating that the wireless friendliness of building materials should be considered in the planning and optimization of indoor wireless networks. The outcomes of this article would enable appropriate selection of wall materials during building design, thus enhancing the capacity of indoor LOS MIMO communications.