Statistical QoS provisioning over D2D-offloading based 5G multimedia big-data mobile wireless networks

As one of essential techniques to support the multimedia transmissions over the fifth generation (5G) mobile wireless networks, the statistical quality of service (QoS) provisioning has been proved to be effective in statistically guaranteeing time sensitive video transmission over time-varying wireless channels. But, how to efficiently design and implement statistical QoS provisioning enabled wireless network remains as the open challenging problem. To overcome these challenges, in this paper we propose the hierarchical-caching architecture to efficiently implement the statistical QoS guarantees in 5G wireless networks, which cooperatively caches the popular multimedia data contents at different wireless network caching tiers, efficiently trading the widely available storage resources for precious wireless bandwidth resources. However, the hierarchical caching scheme imposes the multi-hop wireless link statistical delay-bounded QoS provisioning problem when the requested multimedia data contents are fetched across tiers. In particular, we develop two hierarchical caching schemes to adaptively cache the delay bounded multimedia data contents: (1) caching in mobile users, which yields the single-hop wireless transmission and (2) caching in routers, which yields the tandem two-hop wireless transmission. To maximize the average effective capacity when cached data contents dynamically switch between these two schemes, we apply the Markov chain model to derive the optimal departure rates for data contents leaving cacheable mobile users and routers, respectively. U sing these two departure rates, we develop the optimal caching schemes in terms of the probability distribution of how long to cache the data contents and the probability of where to cache the data contents. Finally, we evaluate and validate these two optimized caching schemes through numerical analyses.

Multimedia big data computing and Internet of Things applications: A taxonomy and process model

The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia‐enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT‐enabled data management systems to handle MMBD including high‐volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision‐making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT‐enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real‐time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture.

Introduction It is envisioned that the capacity demand in fifth generation (5G) wireless networks will increase by 1000 times before the year 2020, and this huge demand will be fueled by high-definition video and content streaming applications, which not only consume immense wireless bandwidth but also have stringent real-time quality of service (QoS) requirements. Small-cell dense wireless networks are regarded as a key candidate 5G technology to meet such aggressive demand. By dense deployment of small-cell base stations (BSs), the network becomes closer to mobile users, and thus the spectral efficiency per unit area can be significantly improved. Moreover, relay stations (RSs) without wired backhaul can also be deployed to enhance the coverage area and provide signal-to-noise ratio (SNR) gain. While there are many potential opportunities associated with dense wireless networks, the potential spectrum efficiency gain they can provide is significantly limited by increasingly severe interference due to the densification of BSs. There are several common schemes to mitigate interference. In the strong-interference case, channel orthogonalization, such as frequency division multiple access (FDMA) or time division multiple access (TDMA), is used to avoid interference [1], and advanced schemes, such as interference alignment (IA) [2, 3] and interference coordination [4, 5], have been proposed to increase the spectral efficiency of interference channels by jointly mitigating the interference using shared channel state information (CSI). However, these approaches may lead to inefficient use of channel resources because the transmitters do not share payload information and therefore are not fully cooperative. To improve the spectrum efficiency further, coordinated multipoint (CoMP) transmission has been proposed as one of the most important core technologies for Long Term Evolution-Advanced (LTE-A) and future 5G wireless networks [6]. By sharing both CSI and payload data among the BSs concerned, CoMP transmission can transform the wireless network from an unfavorable interference topology to a favorable broadcast topology, where the interference can be mitigated much more efficiently. However, the conventional CoMP scheme is quite costly because it requires a high-capacity backhaul for payload exchange between the BSs, and this poses a huge challenge for practical applications of CoMP, especially for small-cell dense wireless networks.

Billions of active online users are continuously feeding the world with multimedia Big Data through their smart phones and PCs. These heterogenous productions are existing in different social media platforms, such as Facebook and Twitter, delivering a composite message in the form of audio, visual and textual signals. Analyzing multimedia Big Data to understand the intended delivered message, had been a challenge to audio, image, video and text processing researchers. Thanks to the recent advances in deep learning algorithms, researchers had been able to improve the performance of multimedia Big Data analytics and understanding techniques This paper presents a survey on how a multimedia file is analyzed, key challenges facing multimedia analysis, and how deep learning is helping conquer and advance beyond those challenges. Future directions of multimedia analysis are also addressed. The aim is to stay objective all through this study, bringing both empowering enhancements and in addition inescapable shortcomings, wishing to bring up fresh questions and stimulating new research frontiers for the reader.

This paper addresses the Quality of Service (QoS) degradation problem of wireless relay node failures in multihop Worldwide interoperable Microwave Access (WiMAX) and Long Term Evolution (LTE) networks. In general, node protection in a communication network increases the reliability of the traffic flow from source to destination. For wireless networks, implementing the traditional 1+1 protection scheme increases the capital cost, as resources cannot be fully utilized. On the other hand, the design and implementation of the 1:N protection scheme in wireless networks for greater value of N (> 2) is very difficult, due to wireless network coverage and the physical layer design. To overcome the above drawbacks, a technique based on network coding is proposed for node protection in multihop wireless networks. In multihop wireless networks, few research efforts are concentrated on node protection schemes using network coding. Moreover, to best of our knowledge, no reports have been published on measuring the QoS performance for node protection in 4G wireless networks using network coding. In this paper, we first describe the node protection scheme using XOR network coding for multihop 4G wireless networks. Then, we measure the QoS performance, such as packet delivery ratio (PDR), latency and jitter, for different scenarios. The scenarios include a failure of a single relay node and failures of two relay nodes with and without our protection scheme and user's mobility. The simulation results show that the QoS performance with protection against a single relay node failure and failures of two relay nodes are very close to that of no failure scenario, while the network reliability increases due to the protection mechanism. Also, we report a decoding problem in multihop wireless networks using XOR network coding.

This paper investigates the model aggregation process in an over-the-air federated learning (AirFL) system, where an intelligent reflecting surface (IRS) is deployed to assist the transmission from users to the base station (BS). Since the successive interference cancellation (SIC) is adopted as a basis to decode local model parameters and analyze their statistic characteristics for detecting malicious devices, the quality-of-service (QoS) requirement is ensured. The objective of this paper is to minimize the mean-square-error by jointly optimizing the receive beamforming vector at the BS, transmit power allocation at users, and phase shift matrix of the IRS, subject to the transmit power constraint for devices, unit-modulus constraint for reflecting elements, SIC decoding order constraint and QoS constraint. To address this complicated problem, alternating optimization is employed to decompose it into three subproblems, where the optimal receive beamforming vector is obtained by solving the first subproblem with the Lagrange dual method. Then, the convex relaxation method is applied to the transmit power allocation subproblem to find a suboptimal solution. Eventually, the phase shift matrix subproblem is addressed by invoking the semidefinite relaxation. Simulation results validate the availability of IRS and the effectiveness of the proposed scheme in improving federated learning performance.

Physical layer security of non-orthogonal multiple access (NOMA) systems has attracted great attentions. However, the impact of mobility on physical layer security of NOMA systems has not been well studied. In this paper, to fill this gap, we investigate the impact of random mobility on physical layer security of NOMA systems. Considering scenarios where a base station (BS) or access point (AP) communicates to two random mobile users with a passive eavesdropper in two concentric circles, we study the secrecy performance with combinations of two typical random mobility models: random waypoint (RWP) and random direction (RD). A general analytical framework to numerically calculate the average secrecy rates of NOMA mobile users under steady state is provided. By comparing secrecy performance of mobile users with static users, we find that RWP mobile users can achieve higher average secrecy rates than the users with other mobility combinations. Meanwhile, two types of secrecy fairness for mobile users are fully considered and we propose a novel sum average secrecy rate maximization problem, subject to average power limits and users' QoS (quality of service) requirements. Considering eavesdropper's channel state information (CSI) is unknown to BS, we propose a threshold power allocation strategy to improve the sum average secrecy rate of NOMA mobile users. Extensive numerical simulations are conducted to validate our model and theoretical analysis.

In this paper, we explore the effect of dual connectivity (DC) on delay-aware resource control with statistical Quality of Service (QoS) provisioning in heterogeneous aeronautical networks. Firstly, the Software Defined Networking (SDN) controller is introduced to support the DC for aircraft by centralized management of different base stations. In light of the delay analysis of user traffic, the delay-aware resource control of SDN controller is established as a multi-objective stochastic optimization problem to guarantee the statistical QoS provisioning. Secondly, the multi-objective stochastic optimization problem is transformed into a weighted single-objective optimization and relaxed by the Lagrangian relaxation. Then, the Lyapunov optimization theory is utilized to solve the relaxed stochastic optimization for optimal resource control. Finally, the performance of resource control under DC mode is verified in two aeronautical scenarios.

The deployment of femtocell is as a potential solution to improving the indoor coverage and capacity of a cellular network. However, providing quality of service (QoS) is one of the most significant challenges in wireless femtocell networks. Cellular users with different QoS requirements always look for a serving base station (BS) to achieve their desired QoS. Therefore, intelligent BS allocation plays a crucial role in guaranteeing the QoS. In this paper, we propose a game-theoretic BS allocation approach with a QoS guarantee for downlink femtocell networks. We model the BS allocation problem as an evolutionary game in which users with bounded rationality learn from the environment and make BS selection decisions with minimum exchange of information. In the propounded model, not only do we consider the users’ QoS, but we also take account of macro users’ activity as an interferer to reduce the cross-tier interference. Unlike the previous studies, we calculate the demand rejection probability for each user associated with a BS to evaluate statistical QoS guarantees. A distributed learning-based algorithm is developed to show the existence and fairness of the solution to our proposed model and to demonstrate the convergence to the evolutionary equilibrium as the solution of the game. Simulation results verify the effectiveness, performance improvement, and good convergence of the proposed approach.

With the evolution of various new types of application services for mobile devices, cellular operators have started providing multiple subscription plans to the mobile users. The plan subscribed determines the Quality of Service (QoS) to be provided to the user, and operators distinguish users as priority users and non-priority users, accordingly. To ensure better QoS to the priority users, necessary resources must be reserved at the base station. This demands analyzing the network traffic to predict future traffic pattern. This paper pioneers the use of machine learning methods to forecast mobile user traffic pattern for providing better QoS to the priority users. We analyze two different supervised learning methods, Naive Bayes Classifier and Logistic Regression, used for prediction of probable times when a priority user would be active. The prediction results are applied to the user scheduling strategies for efficient bandwidth management, thus improving system capacity and reduce blocking. Simulations on multiple real-life datasets validate the model and predict the mobile user pattern with very high accuracy, along with significant reduction in priority user service blocking ratio and improvement in their capacity.

To improve the quality of tourism management classroom teaching and then improve the quality of undergraduate tourism management professionals, this paper analyzes tourism management classroom teaching evaluation based on big multimedia data. The mean and autocovariance matrix are determined by multivariate statistical analysis, and the effective learning behavior evaluation index and survey data are determined by entropy analysis, and then the evaluation index of tourism management classroom teaching quality is established to explore the improvement of teaching quality after big multimedia data is applied to tourism management classroom. According to the statistics related to the basic tourism education and training statistics of the China Tourism Administration, as of 2017, there were 608 general higher education institutions offering tourism management undergraduate majors with a total enrollment of 59,000; 1086 general higher education institutions offering tourism management higher education majors with a total enrollment of 113,000; 947 secondary vocational The total number of enrollment in 947 secondary vocational schools with tourism-related majors is 103,000, and the scale of tourism talent training has expanded, and the education problem of tourism management majors has gradually come to the fore. Therefore, it is timely and necessary to analyze the evaluation of tourism management classroom teaching based on big multimedia data, and to a certain extent, it can promote the improvement of the education level of tourism management majors in colleges and universities.

The fifth-generation (5G), now being developed for use in the millimeter-wave (mm-wave) frequency bands, would enhance the Quality of Service (QoS) for the Mobile User (MU) in the near future. Despite the advantages of using mm-wave communications that are attained through the large bandwidth available in this band, it suffers from high penetration loss and low diffraction, which causes very high attenuation of the signal in free space. The attenuation of the signal can also be due to the blockages, such as buildings, human presence, vehicles, etc. As a result, the signal propagation path could be interrupted by those blockages causing significant fluctuation in the handover (HO) rate. Thus, the foreseen mm-wave communication gains are achieved at the expense of varying HO rates. Therefore, the impact of blockages on the HO rate for user mobility is a crucial performance factor that needs to be analyzed and addressed in mm-wave cellular communication systems. This paper analyses the impact of static blockage on the HO rate for a MU in the 5G mm-wave cellular network. We began by obtaining the impact of blockage with a fixed location in the 2-D plane on the HO rate for a MU moving radially away from the mm-wave base station (BS) at a certain speed. Afterward, we considered the MU moving in a uniform angle with a certain speed in the mm-wave cellular network. The mm-wave BSs, in this study, have been assumed to be distributed according to a homogeneous Poisson Point Process (PPP). The results show that the blockage has a remarkable impact on the HO rate, depending significantly on its location, and MU's direction and speed.

In this paper, we propose a statistical Quality-of-Service (QoS) driven power and rate allocation scheme over wireless Rayleigh fading cognitive radio links. Specifically, we consider the scenario that one secondary link coexists with one primary link by sharing particular portions of the spectrum. Our proposed power and rate allocation scheme aims at maximizing the system effective capacity, which can be seen as the maximum arrival rate supported by the secondary link subject to a given statistical delay QoS constraint. In this work, we not only take into account the average transmit and interference power constraints for the secondary link, but also consider the influence from the transmission of the primary link to the effective capacity of the secondary link. Simulation results show that, (1) the effective capacity of the secondary link decreases when the statistical delay QoS constraint becomes stringent; (2) given the QoS constraint, the effective capacity of the secondary link varies with the interference power constraint and the SNR of the primary link.KeywordsCognitive radiopower allocationstatistical QoS guaranteeseffective capacity

Power allocation is a critical issue in the physical layer of power-domain nonorthogonal multiple access (NOMA) systems. However, existing power allocation schemes have not considered the delay quality of service (QoS) requirement in the datalink layer of users, and hence may not ensure the desired delay QoS requested by the services in the upper layers. Different from existing works, we apply the statistical QoS theory into NOMA systems and formulate the physical-datalink cross-layer power allocation problem as a stochastic optimization problem under different delay QoS constraints. Also, we show that the formulated problem is quasi-concave and propose a bisection-based cross-layer power allocation algorithm. Simulation results show that the proposed algorithm is able to converge to the optimal solution obtained by exhaustive search. Also, the proposed scheme outperforms existing fixed NOMA and time-division multiple access based schemes in terms of max-min effective capacity.