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Predicting the number of views for videos is crucial for optimizing resource allocation and reducing costs in cloud-based video hosting platforms. In this manuscript, we propose a novel method for view count prediction using the ARIMA (AutoRegressive Integrated Moving Average) model. By accurately forecasting video viewership, we aim to minimize the allocation of unnecessary cloud resources while ensuring sufficient resources are available to handle peak demand. Our proposed method leverages historical viewership data to train and fine-tune the ARIMA model, enabling it to capture the underlying patterns and dynamics of video viewership. Through extensive experimental evaluations on a large dataset, we demonstrate the effectiveness of our approach in reducing cloud resource costs. Compared to existing methods, the proposed method achieves an average cost reduction of 25% while maintaining a high level of prediction accuracy. Furthermore, we observe a 15% improvement in resource utilization, indicating better resource allocation based on the predicted view counts. [ABSTRACT FROM AUTHOR]

Employing multi-layer networks represents a promising approach to address the increasing demand for high data rates and efficient congestion control in cellular communication networks. This study focuses on the downlink performance of a two-tier network comprising multiple macro base stations and femto access points, where the spatial distributions of macro base stations, femto access points, and macro users follow Poisson point processes. Also, femto users are uniformly distributed within the coverage area of femto access points, each of which is subject to a backhaul constraint, limiting the number of users it can serve. Consequently, a hybrid access policy is adopted, wherein each macro user is assigned to its closest femto access point or the macro base station, based on a distance ratio criterion. Under this access model, the outage probability experienced by macro users served by femto access points is analyzed, alongside the derivation of the outage probability for macro users served by macro base stations. Both analytical derivations are further supported by computer simulations. [ABSTRACT FROM AUTHOR]

Developing China's high-tech industry is important to make the country innovation-oriented. This requires optimising the allocation of innovation resources and improving innovation efficiency. However, few studies have investigated this topic and the realisation path for the high-tech industry. This study develops an input-oriented inverse data envelopment analysis (DEA) model with frontier changes to analyse the optimisation of resource allocation in China's high-tech industry during 2019–2025. With this method, decision makers can scientifically analyse the specific amount of resource investment. We also construct an analysis framework from short- and long-term perspectives. The results show that the excessive input of research and development (R&D) personnel and unbalanced allocation of capital resources are the main barriers to the development of high-tech industries in the short term, and in the mid- and long terms, the demands for investment in talent and capital will continue to increase. Improvement directions for promoting the development of China's high-tech industry are discussed. Finally, we present valuable information for policymaking to promote progress in high-tech industries in different regions. [ABSTRACT FROM AUTHOR]

Effective sharing of the communication channel among many users, or multiple access (MA) techniques, can play a vital role in meeting the diverse demands of low latency, high reliability, massive connectivity, better fairness, and high throughput. In this context, non-orthogonal multiple access with multiple antennas, also known as multiple-input, multiple-output NOMA (MIMO-NOMA), is a promising enabling technology for fifth-generation (5G) and beyond (5G) wireless networks. The proper design of beamforming systems is one of the major difficulties in developing MIMO-NOMA. There are various ways to design beamforming for MIMO-NOMA in the literature. However, there is not much work dedicated to the survey focusing only on beamforming design in MIMO-NOMA systems. This work presents a comprehensive overview of beamforming methods in MIMO-NOMA for 5G and B5G. These strategies are classified in detail and have varied attributes, benefits, and drawbacks. As a result, future research gaps are also highlighted. Moreover, a simulation study is presented as a case study on the impact of random beamforming in various scenarios of heterogeneous environments with small and macro-cells. For this purpose, users' outage probability is simulated with various types of interference in the heterogeneous systems, including inter-cluster, cross-tier, and co-tier interferences. This analysis also helps to contrast the performance of small and macro-cells. Finally, future research directions are discussed for beamforming in MIMO-NOMA for 5G and B5G. [ABSTRACT FROM AUTHOR]

With advances in Internet of Things (IoT) technologies, billions of devices are becoming connected, which can result in the unprecedented sensing and control of the physical environments. IoT devices have diverse quality of service (QoS) requirements, including data rate, latency, reliability, and energy consumption. Meeting the diverse QoS requirements presents great challenges to existing fifth-generation (5G) cellular networks, especially in unprecedented scenarios in 5G networks, such as connected vehicle networks, where strict data packet latency may be required. The IoT devices with these scenarios have higher requirements on the packet latency in networking, which is essential to the utilization of 5G networks. In this paper, we propose a multi-tier cellular-based IoT network to address this challenge, with a particular focus on meeting application latency requirements. In the multi-tier network, access points (APs) can relay and forward packets from IoT devices or other APs, which can support higher data rates with multi-hops between IoT devices and cellular base stations. However, as multiple-hop relaying may cause additional delay, which is crucial to delay-sensitive applications, we develop new schemes to mitigate the adverse impact. Firstly, we design a traffic-prioritization scheduling scheme to classify packets with different priorities in each AP based on the age of information (AoI). Then, we design different channel-access protocols for the transmission of packets according to their priorities to ensure the QoS in networking and the effective utilization of the limited network resources. A queuing-theory-based theoretical model is proposed to analyze the packet delay for each type of packet at each tier of the multi-tier IoT networks. An optimal algorithm for the distribution of spectrum and power resources is developed to reduce the overall packet delay in a multi-tier way. The numerical results achieved in a two-tier cellular-based IoT network show that the target packet delay for delay-sensitive applications can be achieved without a large cost in terms of traffic fairness. [ABSTRACT FROM AUTHOR]

Summary: The heterogeneous ultra‐dense network (UDN) is a complex network environment where the small cells (SCs) are densely populated to acquire data transmission. The UDN is mainly adopted to deal with the explosive growth of mobile data and its consequential energy consumption issues. The UDN consists of mobile users, restricting the SCs from offering seamless services as the movement may disrupt the transmissions. To provide an effective solution, this paper introduces an energy‐efficient framework that enables effective data transmissions irrespective of the users' mobility. The proposed model considers the clustered SC deployment where the four‐tiered architecture is adopted. The architecture includes a macro base station (BS), microcells, picocells, and femtocells. The SCs are responsible for transferring the data received from the mobile users to the macro BS. The proposed model introduces the hybrid algorithm called the firefly oriented multiverse optimization (FF‐MVO) algorithm to identify the most optimal path for data transmission. This algorithm works iteratively to identify the optimal path to reach the macro BS for each transmission from the user. The proposed model is simulated in the network simulator 3 (NS3) platform, and the results are evaluated with the existing models. The outcomes proved that the proposed algorithm is more optimal than the other models in finding the optimal path to result in energy‐efficient transmissions. The proposed method achieved an average energy consumption of 0.24 J, an average energy efficiency of 10.965 bits/s/J and an average network throughput of 33.907 Gbps. [ABSTRACT FROM AUTHOR]

We introduce a new network centrality measure founded on the Gately value for cooperative games with transferable utilities. A directed network is interpreted as representing control or authority relations between players—constituting a hierarchical network. The power distribution embedded within a hierarchical network can be represented through appropriate TU-games. We investigate the properties of these TU-representations and investigate the Gately value of the TU-representation resulting in the Gately power measure. We establish when the Gately measure is a core power gauge, investigate the relationship of the Gately with the β -measure, and construct an axiomatisation of the Gately measure. [ABSTRACT FROM AUTHOR]

The densification of the orthogonal frequency division multiplexing (OFDM) based fifth-generation communication systems, as well as the requirement of integrating sensing and communication functionalities, has promoted the development of integrated-sensing-and-communication (ISAC) dense cellular networks (DCNs). In the OFDM-based ISAC-DCN, multiple base stations simultaneously serve mobile users and sense targets based on the echo of downlink communication signals. In this paper, we establish the interference model in ISAC-DCN for sensing and communication. Focusing on the interference management of the ISAC-DCN, we investigate the multi-dimension resource allocation problem. In particular, we aim to maximize the network utility by jointly optimizing sub-band allocation, user association, and transmission power under the sensing signal-to-interference-plus-noise ratio constraint to be solved iteratively. The mixed-integer optimization problem is decoupled into three sub-problems. Specifically, a greedy genetic sub-band allocation scheme is proposed for sub-band allocation to reduce total interference. We employ the successive convex approximation technique to transform the transmission power control sub-problem and solve it via geometric programming. Simulation results illustrate the trade-off between sensing and communication performances, and show that the proposed algorithm significantly improves the network utility and achieves higher detection probability. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) technique is a promising alternative for enhancing network sum-rate while increasing the interference experienced by the users. In heterogeneous networks, the signal of a NOMA user is affected by three types of interference; intra-cell, inter-cell, and inter-tier. In an environment with high interference, using orthogonal multiple access (OMA) along with NOMA is beneficial to improve the edge user performance. This work proposes a hybrid model, where NOMA and OMA are deployed together in the same spectrum band. The performance of hybrid model is compared to NOMA and OMA for heterogeneous networks by stochastic geometry analysis. The analysis includes pico- and femto-cell alternatives to elaborate varying interference conditions. Using the capacity analysis formulation of NOMA, a power level optimization framework is presented for maximum sum-rate and fairness objectives. Besides, two pairing methods, near-to-near and near-to-far pairing, are included from the literature to reveal pairing effect on user-specific and system-level performance. The numerical results indicate NOMA trade-offs for increasing edge user capacity by power optimization and resistance of hybrid model to interference under different conditions. [ABSTRACT FROM AUTHOR]

The implementation of directional antennas on either access point or user equipment (UE) operating on mmWave frequencies is mostly essential in mmWave networks. Transmit antenna selection (TAS) is a low hardware and software complexity beamforming mechanism which is currently becoming popular for mmWave UEs due to the small size of antennas in mmWave frequencies. The solution to optimal TAS problem is generally simple for one-tier networks, however, this problem is of high complexity for multi-tier underlay networks, wherein the interference imposed from low-priority secondary users should be controlled in order not to violate quality-of-service requirements of high-priority primary users. There exist two very recent articles in the literature studying the optimal UE-side TAS joint with network resource allocation for multi-tier mmWave device-to-device networks. Such works consider finding the optimal active transmit antennas, channels and transmit powers of all users at each time-step in a proactive manner which is generally of high complexity. In this work, for the case where existing resources need not to be recalculated, we consider two low-complexity reactive TAS together with power and channel allocation schemes for the new users requesting to be registered into the network. We manage to obtain the optimal power for the UEs in a reactive distributed manner and then allocate the optimal TAS and channels through converting the problem into a maximum weighted one-to-one matching game. Numerical results reveal the good performance of our proposed schemes. [ABSTRACT FROM AUTHOR]

5G networks are expected to deliver very high data rates with enhanced quality of service (QoS) at a very low cost. To meet the demands of high data rates and improved QoS, ultra-densification is the key solution used in 5G networks. In this paper we propose a call admission control (CAC) policy by considering the main components of an ultra-dense 5G network, namely, macrocell, picocell and femtocell. The calls in the network are accepted based on the SNIR ranges which help in deciding whether the call will be connected to a macrocell or picocell or femtocell. We also come up with a handover management scheme which regulates the handover of calls between macrocell to macrocell (picocell or femtocell), picocell to macrocell (picocell or femtocell) and femtocell to macrocell (picocell or femtocell). The performance analysis of the proposed CAC policy and the handover management scheme is also conducted in the paper. For this, we construct analytical models with the help of continuous-time Markov chains for the macrocell, picocell and femtocell network to obtain the performance metrics such as blocking probability of new calls and dropping probability of handover calls. The analytical results are further investigated to establish the importance of ultra-densification in 5G network. The proposed analytical models are also validated via simulation. A comparative analysis is also performed between the proposed CAC policy and an existing CAC policy in literature which considers only macrocells and femtocells in the network. [ABSTRACT FROM AUTHOR]

Multilayer networks represent multiple types of connections between the same set of nodes. Clearly, a multilayer description of a system adds value only if the multiplex does not merely consist of independent layers. In real-world multiplexes, it is expected that the observed inter-layer overlap may result partly from spurious correlations arising from the heterogeneity of nodes, and partly from true inter-layer dependencies. It is therefore important to consider rigorous ways to disentangle these two effects. In this paper, we introduce an unbiased maximum entropy model of multiplexes with controllable intra-layer node degrees and controllable inter-layer overlap. The model can be mapped to a generalized Ising model, where the combination of node heterogeneity and inter-layer coupling leads to the possibility of local phase transitions. In particular, we find that node heterogeneity favors the splitting of critical points characterizing different pairs of nodes, leading to link-specific phase transitions that may, in turn, increase the overlap. By quantifying how the overlap can be increased by increasing either the intra-layer node heterogeneity (spurious correlation) or the strength of the inter-layer coupling (true correlation), the model allows us to disentangle the two effects. As an application, we show that the empirical overlap observed in the International Trade Multiplex genuinely requires a nonzero inter-layer coupling in its modeling, as it is not merely a spurious result of the correlation between node degrees across different layers. [ABSTRACT FROM AUTHOR]

Technological advancements, barging economics quest for swift communication. The growing demand for data rate and network coverage has pursued telecommunication companies to expand and upgrade their network for maximum coverage and agile speed. The Long Term Evolution (LTE) networks are based on a homogenous configuration comprised of Macrocells. However, during peak hours these macro base stations respond to few numbers of clients whereas many of the user's requests remain unresponded since these stations lack adequate capacity to tolerate the load of increased traffic on the network. To avoid congestion homogeneous networks are turned into heterogeneous networks by augmenting macro base stations with Low Power Nodes (LPN) using femto, pico, and microcells. We propose a new call handling algorithm in which cells are deployed in a hierarchical manner where the priority of originating calls is given to femtocell. The proposed algorithm was implemented for a realistic scenario in a teaching institution and the performance was assessed and compared for 4 scenarios using 3 user cases. Results show that there is a significant improvement in the number of connections established and throughput for both indoor and outdoor users when we increase LPN for a certain critical area. During peak hours, in the fading environment, there is only a 50% connectivity ratio in presence of a single macrocell for 200 users. After applying our algorithm, the connectivity boosts up to 80% which is further increased by deploying more LPNs. Over a well-organized network, the proposed scheme attested to be more efficient than those formerly used, subsequently contributing satisfying outcomes for the end-users. [ABSTRACT FROM AUTHOR]

The global navigation satellite systems (GNSS) receivers suffer from determining an accurate position estimate in the indoor region due to non-line of sight (Non-LOS) conditions. This paper proposes a novel method of position estimation within the indoor region by using distributed GNSS sensors and a range-azimuth sensor setup. All the GNSS sensors are connected to a central node; the inaccurate position estimates evolved from the Kalman filter (KF) framework are transmitted to a central node as primary data. The deviation between the inaccurate position estimate and the GNSS sensor's actual position is the positional deviation (PD) vector, which needs to be estimated. In the same indoor region, a range-azimuth sensor is also deployed. It estimates the GNSS sensor's physical location in its local coordinates, and these estimates are being transmitted to a central node as secondary data. Further, by using the primary and secondary data, we formulated a PD vector compensation followed by a sequential fusion (SF) framework to derive the precise locations of both GNSS sensors and the range-azimuth sensor by recursively estimating the PD vector. Finally, the Cramer–Rao lower bound (CRLB) for the proposed framework is derived. The simulation results are quantified with the root mean square error (RMSE) and CRLB. • GNSS position estimation algorithm using distributed GNSS and range-azimuth sensor. • Position deviation compensation, sequential fusion proposed to estimate GNSS position. • Cramer–Rao lower bound (CRLB) for the proposed framework is derived. • Results are analyzed by varying number of satellite signals and position deviation. • Proposed algorithm find significant applications in indoor localization. [ABSTRACT FROM AUTHOR]

Two near field models for the analysis of spatially fed planar array antennas are presented, compared and applied to a multi-frequency wideband direct layout optimization for mm-Wave 5G new radio (NR) indoor network coverage. One model is based on the direct application of the radiation equations directly derived from the A and F vector potentials. The second model is based on the superposition of far field contributions of all array elements, which are modelled as rectangular apertures with constant field. Despite the different assumptions made to develop both models, the degree of agreement between them in the computation of the radiated near field is very high. The relative error between the models is equal or lower than 3.2% at a plane 13 λ from the array, and it decreases as the near field is computed further away from the array. Then, the faster model is employed in a general direct layout optimization procedure to shape the electromagnetic near field for application in an indoor femtocell to provide coverage with constant power in a private office. Results show that a magnitude ripple better than 1.5 dB can be achieved in an enlarged coverage area covering the whole n257 band of the 5G NR, corresponding to a 10.7% relative bandwidth. [ABSTRACT FROM AUTHOR]

This article considers the minmax common due-window assignment in group scheduling with resource allocation on a single machine. In this problem, jobs are partitioned into groups. In a group, all jobs are processed contiguously, and a common due-window is assigned to them. First, a case with constant processing times is examined. The objective is to minimize the sum of the maximal costs arising from the common due-window among the jobs in each group. This problem is proved to be polynomially solvable. Next, the problem is extended to the case that includes resource allocation. The processing time of each job is considered as either a linear or a convex function of the quantity of resource allocation. The objective function considers the total resource-consumption cost and sum of the maximal costs. For the special case of group-position-dependent penalties, polynomial-time solutions are presented. [ABSTRACT FROM AUTHOR]

Self-organizing networks (SONs) are considered as one of the key features for automation of network management in new generation of mobile communications. The upcoming fifth-generation mobile networks and beyond are likely to offer new advancements for SON solutions. In SON concept, self-healing is a prominent task which comes along with cell outage detection and cell outage compensation. Next-generation cellular networks are supposed to have ultra-dense deployments which make cell outage detection critical and harder for network maintenance. Therefore, by imitating the ultra-dense multi-tiered scenarios, this study scrutinizes femtocell outage detection with the help of long short-term memory and one-dimensional convolutional neural networks by using time sequences of key performance indicator parameters generated in user equipment. In both the proposed schemes, probable outage-related anomalies in femto access points (FAP) are detected and classified within predetermined time sequence intervals. Moreover, aggregation decision methods are also incorporated into the proposed framework for boosting cell outage detection procedure on FAP level. Our findings show that proposed deep learning approaches outperform existing feed-forward neural networks, and on the average, in more than 80% of the cases the outage states of the femtocells are correctly predicted among healthy and three anomalous states. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mobile blockchain (MB) based mining-as-a-service (MaaS) scheme, MB-MaaS for resource-constrained industrial internet-of-things (IIoT) environments. The scheme addresses the research gaps of fixed static allocation for miners to perform computationally intensive mining tasks through a multi-hop computational offloading (CO) scheme and addresses an auction mechanism for a fair bidding process among the miner nodes. The scheme operates in three phases. In the first phase, a multi-hop CO scheme with a fair incentive policy is formulated for miners. The CO schemes offer guaranteed offloading services to mobile devices from far-edge systems through a chain of neighbor nodes. Then, in the second phase, MaaS is proposed to leverage expensive mining tasks through 5G-enabled pico/femtocells. Integration of 5G allows massive end-to-end device and service connectivity. As IIoT ecosystems have limited memory and compute requirements, MaaS assures that the proposed consensus has a responsive validation and mining time. To make the data exchange in the consensus process lightweight, and allow a large number of sensors to share the data in a lightweight manner, an effective consensus mechanism Lightweight Proof-of-Proximity (LPoP), is proposed that forms group validations instead of single block validation. The data is exchanged through javascript object notation (JSON) format, maintaining a steady transaction rate. MB-MaaS is compared against the existing scheme for parameters bid thresholds and request servicing times, and mining and consensus formation. For example, the request serving time at 12 requests is improved by 56.78%, and a significant improvement of 26.47% is observed for processed blocks; parsing time on average is improved by 7.89%. The comparative analysis suggests that the scheme is more efficient than other competing approaches. • A fair reward and incentive policy is proposed for miner nodes through a multi-hop CO scheme in 5G-MEC. • A 5G-femtocell design is proposed to address latency constraints and expedite resource requirements. • A lightweight consensus scheme Lightweight Proof-of-Proximity (LPoP) is presented for IIoT ecosystems. • The evaluation is carried out in terms of BC node characteristics, collusive bidding, and femtocell design. [ABSTRACT FROM AUTHOR]

• The STCEAN model considers the changes of spatial features in the temporal dimension. • The MAS of two heads is focus on different emotional dimensions attention weight. • The STCEAN model uses focal loss function to reduce sample imbalance. • Leave-Half-Subject-Out (LHSO) cross-validation method to reduce trained time. Emotional detection based on facial micro-expressions is essential in high-risk tasks such as criminal investigation or lie detection. However, micro-expressions often occur in high-risk tasks when people often use facial expressions to conceal their actual emotional states. Therefore, spotting macro- and micro-expression intervals in long video sequences has become hot research. Considering the difference in duration and facial muscle movement intensity between macro- and micro-expression, we propose a novel Spatio-temporal Convolutional Emotional Attention Network (STCEAN) for spotting macro- and micro-expression intervals in long video sequences. The spatial features of each frame in the video sequence are extracted through the convolution neural network. Then the emotional self-attention model is used to analyze the temporal weights of spatial features in different emotional dimensions. The emotional weights in the temporal dimension are filtered for spotting macro- and micro-expressions intervals. Finally, the STCEAN model is jointly optimized by the dual emotional focal loss of macro- and micro-expression to solve the problem of sample unbalance. The experimental results on the CAS(ME)2 and SAMM-LV datasets show that the STCEAN model achieves competitive results in the Facial Micro-Expression Challenge 2021. [ABSTRACT FROM AUTHOR]

This article proposes a one-tier ultra-dense cellular network framework based on the characteristics of directional beamforming to explore the downlink signal-to-interference ratio (SIR) performance. The features of the proposed cellular network include: small base stations (SBSs) are equipped with directional antennas; the beam boresight directions of antennas are adaptively adjusted with the locations of users on the two-dimensional horizontal plane while the boresight directions are perfectly aligned with their associated users. In addition, an angle fading model, which follows from the exponential distribution, is developed to simulate the signals attenuation of interference links. The angle fading of the interference signals depends on the angle offset between the locations of users and the beam boresight directions of their interfering SBSs. With the assistance of stochastic geometry, the analytical expressions of coverage probability and average ergodic rate are derived by combining with the probability density function of angle offset variable. Then, the meta distribution of SIR is obtained to capture the performance changes of individual links for users. The numerical results indicate that the performance advantages of the proposed directional network obviously exceed that of the conventional isotropic antennas case. Monte-Carlo simulations validate the correctness of numerical results. [ABSTRACT FROM AUTHOR]

Summary: The use of small base stations (SBSs) to improve the throughput of cellular networks gave rise to the advent of heterogeneous cellular networks (HCNs). Still, the interleave division multiple access (IDMA) performance in sleep mode active HCNs has not been studied in the existing literature. This research examines the 24‐h throughput, spectral efficiency (SE), and energy efficiency (EE) of an IDMA‐based HCN and compares the result with orthogonal frequency division multiple access (OFDMA). An energy‐spectral‐efficiency (ESE) model of a two‐tier HCN was developed. A weighted sum modified particle swarm optimization (PSO) algorithm simultaneously maximized the SE and EE of the IDMA‐based HCN. The result obtained showed that the IDMA performs at least 68% better than the OFDMA on the throughput metric. The result also showed that the particle swarm optimization algorithm produced the Pareto optimal front at moderate traffic levels for all varied network parameters of SINR threshold, SBS density, and sleep mode technique. The IDMA‐based HCN can improve the throughput, SE, and EE via sleep mode techniques. Still, the combination of network parameters that simultaneously maximize the SE and EE is interference limited. In sleep mode, the performance of the HCN is better if the SBSs can adapt to spatial and temporal variations in network traffic. [ABSTRACT FROM AUTHOR]

This paper investigates the application of non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) transmission in the fifth-generation (5G) of heterogeneous cellular networks (HetNets). Due to the high penetration loss of mmWave, we propose that a small cell base station (SBS) serves small cell user (SCU) equipment in the mmWave band, and a macrocell base station (MBS) serves macrocell users (MCUs) in the microwave band. Cell association, user grouping, and power allocation are fundamental challenges in NOMA-based transmission. We formulate optimization problems for SCU and MCU to maximize the energy efficiency (EE) subject to the required minimum data rates and maximum transmission powers. User grouping algorithms are introduced to demonstrate the significance of selecting the best users. To allocate transmission powers, we formulate an EE maximization problem that is non-convex and NP-hard. We propose to use the Bat algorithm, which is one of the popular and efficient metaheuristic algorithms in solving non-convex problems. Analytical expressions for cell association and outage probabilities are derived. We present Monte Carlo simulation results to validate the analytical expressions and show that: (1) choosing the best user among far users influences the overall EE of system impressively; (2) the optimum values of transmission powers can be obtained by Bat algorithm; (3) the proposed grouping algorithms with power allocation methods outperform the other scenarios in terms of EE and spectral efficiency (SE). [ABSTRACT FROM AUTHOR]

With the continuous development of network communication technology, the domestic network has covered the homes of ordinary people, but the indoor coverage system of the network cable network needs to be further improved. The optical fiber distribution system is a signal coverage system integrating GSM, TD-SCDMA, and other standards. It is mainly composed of system access unit, system expansion unit, and system remote unit. Wireless network is an important communication technology, which is easy to use and can be connected wirelessly over long distances. The purpose of this paper is to study an optical fiber distribution system for indoor coverage design of wireless networks, so as to achieve faster and more efficient coverage. In this paper, a wireless network signal processing algorithm is proposed, and a systematic method for designing the wireless network indoor coverage of the optical fiber distribution system is proposed, and the algorithms are compared and tested. The test results in this paper show that the signal strength obtained by the algorithm in this paper is −54.83 dB, while the traditional artificial design algorithm is only −45.97 dB. In addition, the wireless network base point of the algorithm in this paper is also 3 lower than the traditional one. Moreover, the coverage rate achieved by the algorithm in this paper is 3.17% higher than that of the network cable network base point layout given by the genetic algorithm, and the average received signal strength is 3.34 dB higher. In addition, the algorithm in this paper takes 9.1 s, 11.2 s, and 5.3 s for the three scenarios of underground parking lot, large shopping mall, and residential residential area, while the traditional manual design algorithm takes 87.9 s, 108.8 s, and 51.7 s, respectively. The algorithm in this paper is only about 1/10 of the traditional artificial design algorithm. Therefore, it can be concluded that the coverage effect of the wireless network indoor coverage system of the optical fiber distribution system designed in this paper is significantly improved. [ABSTRACT FROM AUTHOR]

Recently, the 5G as the next‐generation network is a popular research and discussed widely. The architecture of 5G is a heterogeneous network, and it can support more networked types, like the ultra‐dense network, traditional cellular network, and Machine to Machine communication. Although the high frequency and larger bandwidth have been using in 5G, resource allocation is still a critical issue that needs to be discussed and solved. Consider the spectrum resource is limited, but almost all users hope that equipment can get a better quality of services. Therefore, how to manage the spectrum resource and allocation is a big problem. Consider the fast‐growing devices and traffic in the future; hence, task scheduling for UEs to reduce energy consumption will be focused on. To solve resource allocation and minimise energy consumption, the Markov decision chain is proposed to be used to predict the channel state. The modified particle swarm optimization (MPSO) is also used in this paper to find the best task scheduling. The simulation will be used to verify the performance of the mechanism that is used and compare it with PSO and first‐in‐first‐service (FIFS). The result shows the method used can be scheduled for the task efficiently. [ABSTRACT FROM AUTHOR]

Iron nitrides have been recognized as attractive anode candidates for lithium ion batteries (LIBs) due to their high theoretical capacities, high tap densities, cost-effectiveness, multiple electron transfer and good ionic diffusion. The strategies of pore structure engineering and carbon coating have been proven to be effective ways to address the critical issues of iron nitrides as anodes for LIBs. Herein, we synthesized a series of Fe 3 N@NC- x with 3D honeycomb nanostructures by a simple heating-controlled and ammoniation-assisted approach. The Fe 3 N@NC- x as anodes for LIBs display outstanding lithium storage properties with high reversible capacity of 767.8 mA h g−1 at 0.1 A g−1 and long-term cycling performance (814.5 mA h g−1 at 1 A g−1 over 2000 cycles). Such outstanding properties were attributed to the interconnected conductive pathways (honeycomb walls, hexagonal channels) for rapid ionic/electronic transport, the macro-/meso porous structure for the quick penetration of electrolytes and the accommodation of electrode volume variation during the cycles, meso-/micro porous structure for the additional storage of Li+, and efficient utilization of the Fe 3 N nanoparticles unifomly distributed through highly conductive interconnected network. [Display omitted] • A series of Fe 3 N@NC composites were synthesized by a simple heating-controlled and ammoniation-assisted approach. • Honeycomb-like nanostructures can accommodate the volume variation and provide rich nanopores for the additional storage of Li+. • The Fe 3 N@NC composites as anodes for lithium ion batteries display outstanding lithium storage properties. [ABSTRACT FROM AUTHOR]

A novel method based on the metamaterial concept has been proposed to increase the gain of a planar array antenna without increasing the dimensions for millimeter‐wave femtocell implementation which can easily replace expensive macrocells. To implement the proposed method and present its advantages, 16 novel metamaterial unit‐cells named split cylinder metamaterial resonators (SCMRs) have been used to increase the gain of a millimeter‐wave two‐layer array antenna. The dimensions of each SCMR are equal to 0.067 mm3 or 0.0001 λg3, where λg is the guided wavelength at 27.5 GHz. Providing an effective metamaterial environment by the proposed SCMRs increases the gain of the antenna by more than 10 dB in the operating frequencies compared to a sample array antenna in which no metamaterial structures is used. The proposed SCMRs at frequencies of 24.5–32.5 GHz, with the creation of negative permeability and permittivity, have created a metamaterial environment. The frequency range covered by the proposed antenna is 26.5–28.5 GHz for millimeter‐wave femtocell applications. The average gain of the proposed antenna in the operating frequency band is equal to 17 dB. Also, the radiation efficiency at 27.5 GHz is 86%. The final dimensions of the proposed antenna are equal to 0.047 λg3 at 27.5 GHz. To confirm the accuracy of the full‐wave simulation, the proposed two‐layer array antenna was fabricated and its characteristics were measured. There is an acceptable fit between the simulated and measured results. It should be noted that the most important advantage of the SCMR is the improvement of antenna performance without increasing the dimensions. [ABSTRACT FROM AUTHOR]

Enabling global Internet access is challenging for cellular-based Internet of Things (IoT) due to the limited range of terrestrial network services. One viable solution is to deploy IoT over satellite systems for coverage extension. However, operating a hybrid satellite–terrestrial network might incur high satellite bandwidth consumption and excessive service latency. Aiming to reduce the content delivery latency from the Internet-connected gateway to the users, this article proposes a two-tier cache-enabled model with full-duplex transmissions where content caches are deployed at the satellite and ground station. A closed-form solution for the successful delivery probability (SDP) of the files is derived considering the requested content distributions and channel statistics. Then, the SDP performance under common caching policies can be evaluated. The results are also used to optimize cache placement under caching capacity constraints. Numerical results demonstrate the performance improvements of the proposed system over those of single-tier cache-aided and half-duplex transmission systems. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, the large-scale access of intelligence equipment with complex heterogeneity will bring unpredictable spectrum limitation and complex interferences to traditional cellular networks. This situation can be alleviated by device-to-device (D2D) technique which improves spectrum efficiency by reusing cellular resources. In this paper, a resource allocation framework comprising channel allocation and power control based on Stackelberg game is proposed for distributed interference coordination between D2D and cellular communications with quality-of-service guarantee. First, base station matches the channel to D2D pairs on the basis of potential throughput gain. Second, interferences from D2D pairs to cellular users are converted into a penalty for D2D pairs through the interlayer price, and the optimization problem of system throughput is decoupled into multiple subproblems those can be solved in distributed and iterative manner in each D2D pair. Simulation results show that two proposed distributed algorithms of channel allocation and power control on interference coordination perform well in convergence and overall system throughput. [ABSTRACT FROM AUTHOR]

Ad hoc networks cover a variety of paradigms such as mobile ad hoc networks (MANETs), wireless sensor networks (WSNs), vehicular ad hoc networks (VANETs), underwater networks, airborne networks, underground networks, personal area networks, and home networks. In the second paper, "The Time-Free Comparison Model for Fault Diagnosis in Wireless Ad hoc Networks", Jarrah et al. described a new comparison-based model for fault diagnosis in wireless ad hoc networks. B Guest Editorial b An ad hoc network is a wireless system in which network nodes are connected via wireless links and cooperate to self-organize into a network with no need for any infrastructure such as access points or base stations. [Extracted from the article]

Summary: The manifold increase in the number of cellular wireless network consumers has presented a monumental challenge to upgrade the current infrastructure for imparting superior throughput and good quality of service. End‐users of the network will want super high‐speed data with very low latency, as well as secure and with a lower packet data drop rate. Joint coordination between various network resources such as that of a dense heterogeneous network is crucial for achieving the above demands. It is also inevitable that such a dense arrangement of small cells or macro cells or other network equipment will be challenged by different forms of interference apart from being costly and less mobile. Beamforming is among the most advanced technology that is available up to date which has shown good results at handling interference and at guiding transmitted data to the desired end‐user. It is quite useful but also struggles from certain factors when deployed in a dense network. A thorough review of recent works of literature is detailed in the current paper with a focus placed on two major fields, which are interference mitigation techniques and gainful interference exploitation techniques. Some highly advanced interference mitigation techniques which are either developed or under development with their merits have been proposed. Most of these techniques use machine learning and artificial intelligence‐based methods to ensure the best quality of service (QoS) to every individual connected to the network. However some research works focused on negating away any kind of interference present in a signal, some focused on harnessing interference for optimizing the power of transmitted signals. [ABSTRACT FROM AUTHOR]

In addition to the exploration of more spectrum at high-frequency bands, next-generation wireless networks will witness an intelligent convergence of radio frequency (RF) and non-RF links such as optical and visible light communication. Optical attocell (OAC) networks provide an additional layer to RF-based wireless networks with gigabit-per-second data transmission rate and centimeter-level location accuracy. However, the directionality, line-of-sight constraints, as well as strong sensitivity to the location and orientation of user terminals challenge the stringent requirements for throughput and latency. In this article, we consider mobile heterogeneous networks (HetNets) incorporating indoor OAC with femtocells and macrocells to provide a low-cost and energy-efficient solution. The HetNets solution satisfies diverse service requirements in terms of user-experienced data rate, mobility, latency, accuracy, and security in the Internet of Things. To support seamless connectivity and optimal resource allocation in the proposed HetNets with mobility awareness, handover in dynamic environments needs to be addressed efficiently. Incorporating rich environmental parameters into such a decision making problem facilitates the self-optimization process, but extensively expands the state space. To achieve a fast convergence speed, a deep reinforcement learning approach is proposed to optimize the handover parameters (e.g., time-to-trigger and hysteresis margin). This is a model-free and off-policy reinforcement setting that trains and employs a deep neural network to predict future rewards for successions of states and actions. Thus, the optimal parameters are obtained by selecting the best actions to take. Through numerical simulation and performance analysis, we discover the gain from enriching the state space and the adaptability of the system to dynamic environments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) can effectively work as temporary base station or access point in the air to transfer/receive data to/from ground terminals (GTs). However, UAV-GT links might be blocked by ground obstacles, like buildings in urban area, leading to a poor performance on data transferring rate. To address this problem, reconfigurable intelligent surface (RIS), as a promising technique, can intelligently reflect the received signals between UAV and GT to significantly enhance the communication quality. Under this deployment of RIS-assisted UAV, we intend to jointly optimize the 3D-space of the UAV and the phase-shift of the RIS to maximize the data transferring rate of the UAV, while minimizing the UAV propulsion energy. The joint problem is non-convex in its original form and difficult to be timely solved by using traditional method, like successive convex approximation (SCA). Therefore, to facilitate the online decision making to this joint problem, we leverage deep reinforcement learning (DRL) to learn the near-optimal solution, and the well known Double Deep Q-Network (DDQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are ultilized. Numerical results show that DRL can effectively improve the energy-efficiency performance of the RIS-Assisted UAV system, compared with benchmark solutions. [ABSTRACT FROM AUTHOR]

The energy efficiency (EE) of femtocells is always limited by the surrounding radio environments in heterogeneous networks (HetNets), such as walls and obstacles. In this paper, we propose to deploy reconfigurable intelligent surfaces (RISs) to improve the EE of femtocells. However, perfect channel state information is more difficult to obtain due to the passive characteristics of RISs and non-cooperative relationship between different tiers. Besides, the low-cost transceivers and reflecting units suffer nontrivial hardware impairments (HWIs) due to the hardware limitations of practical systems. To this end, we investigate a realistic robust beamforming design based on max-min fairness for an RIS-aided HetNet under channel uncertainties and residual HWIs. The joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase-shift matrices of RISs is formulated as a non-convex problem to maximize the minimum EE of the femtocell subject to the constraints of the maximum transmit power of FBSs, the quality of service of users, and unit modulus phase-shift constraints of RISs. We develop an iterative block coordinate descent-based algorithm which exploits the semi-definite relaxation, the S-procedure, and the singular value decomposition method. Simulation results reveal that the proposed algorithm outperforms existing algorithms in terms of fairness, EE, and outage probability. [ABSTRACT FROM AUTHOR]

In this paper, an aerial heterogeneous wireless network consists of a high altitude platform (HAP) and low altitude platforms (LAPs) as aerial base stations (ABSs) in a downlink orthogonal division multiple access (OFDMA) transmission is investigated. Two types of spectrum sharing, including spectrum underlay access (SUA) and spectrum overlay access (SOA) are employed to evaluate the inter-tier interference. Distributed problem formulation and solving method are used to reduce the computational complexity, communication overhead and system latency. Subcarrier assignment and power allocation in spectral efficiency (SE), energy efficiency (EE) and transmission power optimization problems are accomplished by the proposed two-layer algorithm. In the proposed algorithm, first, subcarrier assignment based on constraints of the optimization problems is carried out to cover both SUA and SOA with low computational complexity. Second, the proposed algorithm performs power allocation. The simulation results show the relation between spectrum reuse and inter-tier interference, such that system SE, EE and the number of served users are maximized when both spectrum reuse and inter-tier interference management are performed together. [ABSTRACT FROM AUTHOR]

This paper is dedicated to solving the problem of Advanced Persistent Threat (APT) attack and defense in the Industrial Internet of Things (IIoT). Due to the diversity of IIoT equipment and the inconsistency of protection capabilities, it is difficult for the existing uniform defense strategy and the random defense strategy to achieve ideal results. Considering that both attackers and defenders aim to achieve maximum benefits by paying the minimum cost, as well as the differences between devices, this paper proposes an equipment classification based differential game method for APT in IIoT. Firstly, all equipment is divided into two categories according to their protective capabilities. Secondly, the APT attack and defense process is mathematically described, and the corresponding differential game problem is formulated and analyzed theoretically. Finally, the theoretical results of this method are verified by various experiments, including the comparisons with the uniform defense strategy, the random defense strategy, and the latest model. • A new equipment-based evolutionary dynamics model is proposed. • A necessity system is established to narrow the scope of searching for Nash equilibrium. • An algorithm is designed to generate the Nash equilibrium. [ABSTRACT FROM AUTHOR]

In order to improve the rationality of the actual base station deployment, for the base station deployment of the heterogeneous cellular network（HetNet), this paper considered the repulsion among MBSs, and the repulsion between MBS and PBS. First, this paper used the approximate SIR analysis method of cellular network to derive the coverage probability of the two-tier non-Poisson network. Next, it analyzed the network energy efficiency and derived the throughput and total network energy efficiency. Finally, it proposed an energy efficiency optimization algorithm for the transmission power, which maximized the network energy efficiency by optimizing the transmission power of the PBS. The simulation results show that, the coverage pro-bability of proposed model is better than that of the two-tier HPPP model, and the energy efficiency optimization algorithm can increase the system energy efficiency by about 30%.In actual deployment, people should set the appropriate value of transmit power for PBSs, and control the distance among base stations. [ABSTRACT FROM AUTHOR]

In Today's pandemic situation, 'Spectrum accessing and smart usage' is the sacred Mantra uttered by every individual citizen in the world. Work from home for techies, online classes for students, games for kids, webinar for teaching fraternity etc., are going almost on indoor coverage without any limit in pace because of the smart spectrum coverage by the network service providers. This paper provides an add-on facility to the existing wireless infrastructure to provide a better user experience in this highly regrettable routine. In this paper, a cognitive domain unused spectrum holes are efficiently handled by (i) adaptive spectrum management technique; (ii) Fuzzy Inference System based spectrum administration and (iii) Hybrid Cognitive Femtocell approaches based on the user demand and their applications. The proposed integrated cognitive femtocell and Fuzzy-based approach reduces the indoor coverage problems and enhances the throughput of the macrocell users by allowing adaptive spectrum management based on the demand, thereby eliminating spectrum underlay and overlay problems during critical conditions. In cognitive femtocell networks, the access points are prepared and installed with Cognitive Radio which can determine spectrum dynamically by macrocells and nearby Femto Access Points. It adjusts its radiating parameters to evade the macrocells' interferences and the neighbouring femtocells, thereby maximising the spectrum band's overall utility. [ABSTRACT FROM AUTHOR]

Heterogenous networks are mainly limited by interference. Nowadays, the advances in reconfigurable antennas allow us to implement blind interference alignment (BIA) schemes, which avoid the need for channel state information at the transmitter (CSIT) while providing an increasing multiplexing gain, i.e., the achievable DoF, as the number of users increases. In this work, we propose a downlink transmission scheme based on BIA for managing the inter-tier interference in $K$ -tier networks, referred to as tier BIA (tBIA). The tBIA scheme can be implemented considering that each tier employs any BIA scheme for managing the intracell and intercell interference. In this sense, considering proper BIA schemes applied to each tier, tBIA fully cancels all the sources of interference. After that, the DoF outer-bound for $K$ -tier networks without CSIT is derived. It is shown that tBIA reaches this outer-bound. Furthermore, it is demonstrated that fully managing the inter-tier interference provides larger DoF in the whole network than turning off any tier with the aim of improving the achievable DoF in the remaining tiers. Simulation results show that the proposed tBIA scheme provides greater DoF and improves the user rates in comparison with other schemes without CSIT in multi-tier networks. [ABSTRACT FROM AUTHOR]

The heterogeneous presentation of inattentive and hyperactive-impulsive core symptoms in attention deficit hyperactivity disorder (ADHD) warrants further investigation into brain network connectivity as a basis for subtype divisions in this prevalent disorder. With diffusion and resting-state functional magnetic resonance imaging data from the Healthy Brain Network database, we analyzed both structural and functional network efficiency and structure-functional network (SC-FC) coupling at the default mode (DMN), executive control (ECN), and salience (SAN) intrinsic networks in 201 children diagnosed with the inattentive subtype (ADHD-I), the combined subtype (ADHD-C), and typically developing children (TDC) to characterize ADHD symptoms relative to TDC and to test differences between ADHD subtypes. Relative to TDC, children with ADHD had lower structural connectivity and network efficiency in the DMN, without significant group differences in functional networks. Children with ADHD-C had higher SC-FC coupling, a finding consistent with diminished cognitive flexibility, for all subnetworks compared to TDC. The ADHD-C group also demonstrated increased SC-FC coupling in the DMN compared to the ADHD-I group. The correlation between SC-FC coupling and hyperactivity scores was negative in the ADHD-I, but not in the ADHD-C group. The current study suggests that ADHD-C and ADHD-I may differ with respect to their underlying neuronal connectivity and that the added dimensionality of hyperactivity may not explain this distinction. [ABSTRACT FROM AUTHOR]

Cloud service providers employ virtualization technologies in order to deliver energy efficiency and optimize resource utilization in cloud datacenters. In this way, Virtual Machine (VM) consolidation is an efficient technique for reducing energy related costs and environmental concerns. This technique consists of placing as much as possible VMs on as less as possible Physical Machines (PM)s by means of live VM migration. However, VM consolidation approaches bring advantages, they cannot efficiently guarantee performance isolation of co-located VMs resulting in operational interference issues. Therefore, it is necessary to be aware of workload performance degradation issues during VMs consolidation in a datacenter. In this paper, we propose a novel joint profit and interference-aware scheduling scheme (PIAS) to efficiently consolidate VMs that host multi-tier application workloads in Infrastructure-as-a-Service (IaaS) cloud datacenter. The PIAS scheme considers profit and cost of power consumption, operational interference of VMs, resource utilization, and service level agreements (SLA)s as well as amount of transferred memory pages during live VM migration for consolidating VMs. In this regard, a stochastic dynamic programming is introduced to model the operational behavior of VM. Also, an optimization problem is introduced to maximize provider's profit and minimize the overall execution cost of application workloads. We simulate a series of experiments based on real workloads traces for different IaaS cloud datacenter configurations. The PIAS scheme on average provides better results than other competitors and the results demonstrate how our approach at least improves profit, energy efficiency, and service down time by 40%, 29%, and 35%, respectively. [ABSTRACT FROM AUTHOR]

Future cellular communication systems might be based on millimeter wave frequencies. So, there is a need to design and deploy antennas which operate in the millimeter wave spectrum integrated in mobile terminals, portable wireless devices, base stations and access points. Even though a lot of articles exist on the design of such antennas, fabrication and measurement nuances of such antennas are addressed rarely in the open literature. Initially, the fabrication process of printed circuit board (PCB) antennas is discussed in detail with sources of error and possible precautions. A case study of microstrip-based tapered slot antenna operating at 28 GHz is presented. The variations in the antenna characteristics with slight variations due to fabrication errors are presented. A detailed discussion on S-parameters, pattern and gain measurements is also presented along with possible sources of errors associated with them. [ABSTRACT FROM AUTHOR]

To enhance the performance and the coverage area of the next‐generation heterogeneous wireless networks (HetNets), smaller cells such as femtocells are deployed. A reasonable resource allocation strategy is crucial for the operation of such systems. In this work, the Nash bargaining solution (NBS), Kalai–Smorodinsky bargaining solution (KSBS), and a proposed bargaining solution, which is the combination of NBS and KSBS, are studied to ensure the efficiency and fairness in the transmission rate allocation among femtocells. The mentioned bargaining solutions are optimised subject to the rate constraints due to the Han–Kobayashi (HK) rate splitting scheme, and a maximum tolerable interference caused by femtocell base stations (FBSs) at the macrocell user equipment. Two methods are used to address the bargaining solutions' optimisations. First, the conventional optimisation‐based method is used for the situations where the channel gains are known, then a reinforcement learning‐based algorithm for a real environment is proposed without being aware of the channel gains. To accelerate the speed of the learning process and to initialise the values of quality functions, the hotbooting technique is used. The bargaining solutions are compared by fairness and efficiency criteria in both methods. Numerical simulations show that the proposed reinforcement learning‐based algorithm achieves the same performance derived from the conventional optimisation method. First, this paper studies the two well‐known Nash and Kalai–Smorodinsky bargaining solutions as the good tools to establish fairness in rate allocation problem among femtocells in a HetNet. Then a new bargaining solution is proposed as the combination of the two bargaining solutions. The fairness and the efficiency of the allocated rates to femtocells as the output of optimisation problems of bargaining solutions are compared via Jain index criterion (as the fairness metric) and inverse price of anarchy criterion (as the efficiency metric). Moreover, the femtocell base stations are allowed to use Han–Kobayashi signalling scheme to send their signals in private and common messages which results in the best‐known achievable rate region. Two methods are used to solve the optimisation problems: the conventional optimisation method and the reinforcement learning‐based method (to formulate a real‐world model with the unknown channel gains). It is shown that the proposed bargaining solution (in both methods) results in fairer rates than Nash and Kalai–Smorodinsky bargaining solutions while all of them are efficient. [ABSTRACT FROM AUTHOR]

In the mobile edge computing environment, edge servers, edge base stations and other intermediate servers have limited computing, storage resources and limited coverage, resulting in increased content access delays and reduced user experience quality. Content caching methods based on file value and corresponding algorithms are proposed. This method uses Markov chains to predict the mobile user's trajectory in the next time slot, and active caching and cache replacement algorithms are designed to minimize content access latency. In addition, because edge servers are connected to many different access points or base stations, whether or not to migrate the ongoing edge services and where to migrate the services are the key issues to consider when any user moves outside the service area of the related edge servers. To reduce the overhead of service migration due to the mobility of users and the complex topological relationship between edge servers, a de-centralized service migration algorithm is proposed, which integrates the analysis and selection of the optimal edge base station and service placement decisions with the objective of minimizing the overhead of service migration. The experimental results show that the proposed edge caching and service migration methods reduce content access latency and service migration overhead. [ABSTRACT FROM AUTHOR]

In Orthogonal Frequency Division Multiple Access based cellular networks, deployment of femtocell is one of the promising developments that can enhance the capacity and coverage of indoor users. However, the deployment of femtocells imposes severe cross-tier interference which degrades the overall system performance. This kind of interference may occur when femtocells are sharing the spectrum of macrocell in a co channel approach. Therefore, an efficient resource partitioning scheme is essential to minimize the effect of cross-tier interference in two-tier Heterogeneous Network. In this research, a Modified Region splitting based Resource Partitioning (MRRP) scheme is proposed to achieve the objective. In this scheme, the whole macrocell coverage area is divided into inner, centre and outer regions. In a cluster of three cells, the total available spectrum is divided into four sub bands. The first three sub bands are shared between inner and outer region. The fourth sub band is further divided into three sub bands which is utilized by centre region. In MRRP scheme, the unused sub band of each macrocell is assigned to femtocell in three different approaches namely static, order and random. The impact of these appproaches on the sum throughput of femtocell, average per user throughput, and total throughput is investigated. Further, the optimization of the proposed MRRP schme is achieved using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

To exploit the power domain diversity, heterogeneous non-orthogonal multiple access (NOMA) networks improve the spectrum efficiency for Internet of Things (IoT). For video traffic transmission, we formulate a resource allocation and assignment problem as a mixed integer non-linear programming (MINLP) subject to video encoding characteristics, maximum number of accessed devices, max-min fairness criterion, and total available energy of each device. To solve the resource allocation and assignment problem over heterogeneous NOMA networks, two subproblems are formulated, i.e., a packet assignment subproblem for video traffic and a joint device allocation and power control subproblem. Firstly, the joint device allocation and power control subproblem is transformed into a bi-convex programming with successive convex approximation (SCA) method. Then, an optimal device allocation and power control solution is obtained via dual decomposition method. Finally, a heuristic packet assignment algorithm via greedy method is presented for video transmission traffic. In numerical simulation, we can see that the proposed algorithm guarantees the max-min fairness among different devices, and improves the minimum and average video transmission quality over heterogeneous NOMA networks. [ABSTRACT FROM AUTHOR]