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The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

Terahertz (THz) systems have the benefit of high bandwidth and hence are capable of supporting ultra-high data rates, albeit at the cost of high pathloss. Hence they tend to harness high-gain beamforming. Therefore a joint hybrid 3D beamformer relying on sophisticated sensor-based beam training and channel estimation is proposed for Reconfigurable Intelligent Surface (RIS) aided THz Multi-user Massive Multiple Input Multiple Output (MIMO) systems. A novel joint subarray based THz base station (BS) architecture and the corresponding sub-RIS is proposed. The BS, RIS and receiver antenna arrays of the users are all Uniform Planar Arrays (UPAs). Moreover, the conditions of maintaining the orthogonality of the proposed joint architecture are derived in support of spatial multiplexing. The closed-form expressions of the near-field and far-field path-loss are also derived. The Ultra-wideband (UWB) sensors are integrated into the RIS and the user location information obtained by the UWB sensors is exploited for channel estimation. The optimal active and passive beamforming schemes are also derived. Moreover, Precise Beamforming Algorithm (PBA) for joint RIS phase shift and user equipment (UE) receiver beamforming is proposed, which further improves the beamforming accuracy by circumventing the performance limitations imposed by positioning errors. Our simulation results show that the proposed system significantly improves the spectral efficiency, despite its low complexity. Compared to the scheme operating without PBA, our proposed scheme increases the spectral efficiency on average by 10.41%, 10.17%, and 5.19% for the three farfield configurations, and by 5.05% and 3.95% for the two nearfield configurations, respectively. This makes our solution eminently suitable for delay-sensitive applications. [ABSTRACT FROM AUTHOR]

In this paper, we theoretically analyse the uplink (UL) and downlink (DL) performance of massive multiple-input and multiple-output (mMIMO) networks, in term of coverage probability, cell spectral efficiency and network area spectral efficiency, using stochastic geometry. A sophisticated but yet practical system model is considered, taking into account a path loss model differentiating line-of-sight and non-line-of-sight transmissions, an idle mode capability at the base stations and a finite user density. Our analysis pays particular attention to the existence of a finite number of UL pilots for channel estimation and the effect of pilot contamination. We study for the first time the joint impact of the number of UL pilot sequences, the user density, and the base-station density on the pilot contamination issue in a mMIMO network, which in turn characterizes the DL and UL network performance. Moreover, using the proposed framework, we investigate two different scheduling problems—UE and pilot scheduling—, to find the optimal simultaneously scheduled UE density per time-frequency resource as well as the optimal UL pilot number to maximise the spectral efficiency. [ABSTRACT FROM AUTHOR]

Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset. [ABSTRACT FROM AUTHOR]

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users’ private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework. [ABSTRACT FROM AUTHOR]

Low-density parity-check (LDPC) codes that allow for encoding-decoding have been adopted in the fifth-generation (5G) network to support high-throughput and low-latency communications. To meet new user requirements of the beyond 5G and future sixth-generation (6G) wireless networks, reconfigurable intelligent surface (RIS) has been proposed as a promising solution to enhance the communication quality. In this letter, we investigate the performance of a RIS-assisted wireless communication system where the signal transmitted from the transmitter is protected by LDPC codes. We derive the analytical upper and lower bounds on the bit error probability of the RIS-assisted wireless system with LDPC-coded source signals. Through Monte Carlo simulations, we validate the accuracy of our derived bounds for various numbers of RIS reflecting elements. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals. [ABSTRACT FROM AUTHOR]

Wearable devices are increasingly popular for health monitoring via electrocardiograms (ECGs) as they can portably monitor heart conditions over a long time. However, so far there are no publicly available ECG data sets collected from wearable devices. Most ECG analysis algorithms target ECG data collected by hospital equipment. In the present study, we used the IREALCARE2.0 Flexible Cardiac Monitor Patch as the wearable device to collect ECG signals and formed ECG data sets. Wearable ECG data tended to contain more interference and be large in size. This article proposed a deep CNN approach, named time–spatial convolutional neural networks (TSCNNs), for the automatic classification and analysis of ECG signals from wearable devices. First, the original long-term ECG signals were divided into separate heartbeats and input into the TSCNN. Second, we applied convolution over time and spatial filtering for each heartbeat to extract abundant features. Finally, the cascaded small-scale kernel convolution was applied to improve classification performance and reduce the number of network parameters. To avoid overfitting, some regularized methods such as dropout and batch normalization were adopted. In the experiments, the method proposed in this letter is compared with other eight ECG classification algorithms. Our method attained the highest classification accuracy. The experimental results indicated that the proposed method can achieve better performance for wearable ECG data and can effectively monitor whether the wearer has an abnormal ECG. [ABSTRACT FROM AUTHOR]

We discover a new capacity scaling law in ultra-dense networks under practical system assumptions, such as a general multi-piece path loss model, a non-zero base station to user equipment antenna height difference, and a finite user equipment density. The intuition and implication of this new capacity scaling law are completely different from those found in the year 2011. That law indicated that the increase of the interference power caused by a denser network would be exactly compensated by the increase of the signal power due to the reduced distance between transmitters and receivers, and thus, network capacity should grow linearly with network densification. However, we find that both the signal and interference powers become bounded in practical ultra-dense networks, which leads to a constant capacity scaling law. Moreover, our new discovery on the constant capacity scaling law indicates three network optimization problems respectively for base station deployment, user equipment scheduling and base station coordination. These three optimization problems are justified and solved in this paper, shedding new light on the deployment and optimization of ultra-dense networks. [ABSTRACT FROM AUTHOR]

Terahertz (THz) transmission technologies constitute a promising candidate for supporting ultra-broadband short-range next generation communications. Hence, we analyse the performance of unmanned aerial vehicle (UAV) in the THz networks. The coverage probability is derived as well as the area spectral efficiency (ASE) and a pair of Line-of-sight (LoS)/non-line-of-sight (NLoS) probability models, namely the macrocell LoS/NLoS probability model and picocell LoS/NLoS probability model are adopted. Furthermore, the lower-bound of the network performance are derived via homogeneous Poisson point process (HPPP) analysis, as well as the upper-bound. The simulation results match the analytical results well, which show that the coverage probability of the network first increases upon increasing the THz UAV BS density, and then decreases beyond the maximum. Given the severe path loss experienced by THz signals, a higher UAV density is required for a certain coverage probability than at lower carrier frequencies. [ABSTRACT FROM AUTHOR]

Managing the distributed energy resources in cybersecure smart grids will involve placing trust in organizations that act between operators and proactive consumers. We present a Stackelberg game in a three-party neighborhood area network in conjunction with open access and preservation of private data. [ABSTRACT FROM AUTHOR]

Very recent studies showed that the area spectral efficiency (ASE) of downlink cellular networks will continuously decrease and finally crash to zero as the base station (BS) density increases toward infinity if the absolute height difference between BS antenna and user equipment antenna is larger than zero. Such a phenomenon is referred to as the ASE crash. We revisit this issue by considering optimizing the BS antenna downtilt in cellular networks. It is common to adjust antenna pattern to tune the direction of the vertical beamforming and thus increasing received signal power and/or reducing inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the BS antenna downtilt and the downlink network performance in terms of the coverage probability and the ASE. Our results reveal an interesting find that there exists an optimal antenna downtilt to achieve the maximum coverage probability for each BS density. Numerically solvable expressions are derived for such optimal antenna downtilt, which is a function of the BS density. Our numerical results show that after applying the optimal antenna downtilt, the network performance can be significantly improved, and hence the ASE crash can be delayed by nearly one order of magnitude in terms of the BS density. Our results also give guidance on setting the optimum downtilt angle to maximize network performance given a fixed BS density. [ABSTRACT FROM AUTHOR]

Caching the most popular contents at the wireless network edge such as small-cell base stations (SBSs) is a smart way of reducing duplicated content transmissions and offloading the mobile data traffic in the network backhaul. Currently, most small-cell caching strategies are conceived, designed, and optimized based on the global content request probability (GCRP), with very limited consideration of the individual content request probability (ICRP) reflecting personal preferences. To enable more efficient wireless caching, in this paper, we propose a novel localized deterministic caching framework, drawing upon the recent advances in recommendation systems based on machine learning techniques. By introducing the concept of the rating matrix, we first propose a new Bayesian learning method to predict personal preferences and estimate the ICRP. This crucial information is then incorporated into our caching strategy for maximizing the system throughput, or equivalently, minimizing the download latency, where a deterministic caching algorithm based on reinforcement learning is proposed to optimize the content placement. To this end, we extend the framework to enable device-to-device (D2D) connections to further reduce the download delay, and also design a feedback mechanism to improve the accuracy in the ICRP estimation. Our simulation results verified that with the estimated ICRP and the proposed caching strategy, the proposed framework can significantly outperform the existing methods in terms of hit rate and system throughput. [ABSTRACT FROM AUTHOR]

This article reviews state of the art channel coding techniques for URLLC. The stringent requirements of URLLC services, such as ultrahigh reliability and low latency, have made it the most challenging feature of 5G of mobile networks. The problem is even more challenging for services beyond the 5G promise, such as tele-surgery and factory automation, which require latencies less than 1ms and packet error rates as low as 10-9. This article provides an overview of channel coding techniques for URLLC and compares them in terms of performance and complexity. Several important research directions are identified and discussed in more detail. [ABSTRACT FROM AUTHOR]