Mobile User Equipment Research Articles

Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function

Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE's experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE's Signal-to-Interference-plus-Noise-Ratio (SINR), cells' load and UE's speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms.

Cluster-Based Load Balancing Algorithm for Ultra-Dense Heterogeneous Networks

In a highly dense heterogeneous cellular network, the loads across cells are uneven due to random deployment of cells and the mobility of user equipments (UEs). Such unbalanced loads result in performance degradation such as throughput and handover success. In order to solve the uneven load problem for better network performance, we propose a cluster-based mobility load-balancing algorithm for heterogeneous cellular networks. Traditional mobility load balancing (MLB) schemes that consider only the adjacent neighbors cannot provide enough improvement in network performance. On the other hand, the previous MLB schemes consider neighbors in the entire network suffer from unnecessary MLB actions. However, in the load balancing process, the proposed algorithm considers overloaded cells and their neighbors within then-tiers. First, the algorithm models the network as a directed multi-graph and constructs clusters taking the overloaded cells and their n-tier neighbors. Therefore, by adjusting cell individual offset parameters of the cells in the clusters the algorithm achieves load balancing locally. Since load balancing is performed inside the clusters, the network can be optimized more efficiently by avoiding unnecessary MLB actions. Simulations show that the proposed algorithm distributes the load across the network more evenly than other MLB algorithms, and in a low UE velocity scenario, it increases the overall network throughput by 6.42% compared to a non-optimized network without an MLB algorithm.

Non-Asymptotic Linear Growth of Energy Efficiency in Distributed Autonomous D2D MIMO Wireless Communications

Device-to-device (D2D) communications is a cell-free enabler of 5G and rising wireless communications demand between low-orbit satellite constellation, self-driving vehicles and unmanned aerial systems. Distributed computation (where channel state information is not available) schemes facilitate direct exchanges between mobile user equipment (UE) and if necessary a UE can serve as a relay. Thus, it is essential to develop robust D2D communications frameworks agnostic to the channel distribution which can deliver scalable data rates to self-driving vehicles while the spectral efficiency(SE) is non-asymptotic and energy consumption is increasing to yield an energy efficiency (EE) that is a unimodal function. In this paper, we evaluate the optimal EE by using the computation of the SE in distributed multiple input- multiple output (MIMO) wireless communications. A generalized water-filling framework over arbitrary channel distribution is used to evaluate the SE and then the EE to compare with single-input single-output optimized approximate schemes based on Taylor expansion. The computation yields the optimal power allocation at the transmitters which is derived to illustrate the performance gain of the generalized water-filling distributed MIMO over the approximate scheme. Our simulations results show novel achievable EE linear growth as a function of SE and robust trade-offs in performance gains determined by the total power consumption optimal profile when switching MIMO dimensions. Our trade-off prevents power consumption while EE is decreasing. Thus by switching the number of MIMO operating antenna elements at both the receiver and transmitter sides, EE is no more a unimodal function as illustrated in the literature.

Introduction to the Computation Offloading from Mobile Devices to the Edge of Mobile Network

This paper introduces the concept of Small Cell Cloud (SCC) composed of multiple Cloud-enabled Small Cells (CeSCs), which provide radio connection for mobile User Equipment (UE) such as smart-phones or wearables such as smart glasses. Moreover, CeSCs host computations offloaded from UEs in a way similar to centralized cloud, yet different in its proximity to users. Proposed client-server architecture of SCC conveys mechanisms for moving offloaded computations from the UEs to CeSCs. Real-life implementation of the SCC architecture relies on custom-developed Offloading Framework which is responsible for low-level communication between the UE and the SCC. The Offloading Framework is accompanied by an Augmented Reality (AR) app, which employs intensive computations for discovery of places of interest. Such app is latency-sensitive, a~criterion which makes computation offloading beneficial due to its ability to decrease latency. The combination of the Offloading Framework and the AR app makes up an SCC testbed used for further performance evaluation. Numerous measurements are carried out to examine the impact of various parameters. Based on Proof-of-concept implementation and thorough measurements, it has been revealed that computation offloading can decrease overall latency as much as to 47% and energy consumption on the UE side to 56%.

Performance Analysis of D2D-Aided Underlaying Cellular Networks Based on Poisson Hole Cluster Process

D2D (Device-to-Device) communication has become a crucial part of 4G and 5G networks. It can not only decrease the transmit power of user equipments (UEs) and increase area spectral efficiency of cellular networks, but also offload traffic from cellular networks and alleviate scarcity of spectrum resources by using in-band communication. In this paper, we consider a real tele-communication scenario in which the mobile UEs are primarily located in hotspots and cell edges. By integrating Poisson Hole Process with Thomas Cluster Process, a kind of D2D-Aided Underlaying Cellular Networks (D2D-UCNs) is built. In the D2D-UCNs, we first investigate the aggregation interference of the typical UE and D2D user by using stochastic geometry. Subsequently we analyze their coverage probability and area spectral efficiency. The simulation results show that the coverage probability of D2D users and UEs can be effectively guaranteed by establishing exclusion zone, whilst the area spectral efficiency of the D2D-UCNs can be maximized by optimizing the number of simultaneously active D2D links.

A review of mobility management entity in LTE networks: Power consumption and signaling overhead

SummaryMobility management (MM) in Long‐Term Evolution (LTE) networks is a vital process to keep an individual User Equipment (UE) connected while moving within the network coverage area. MM Entity (MME) is the LTE component responsible for tracking and paging procedures and controlling the corresponding signaling between the UE and its serving cell, which is necessary for data‐packet exchange. Because of the massive increase in the density of mobile UEs, MME is burdened by the high volume signaling load, especially because most of that load comes from Tracking Area Update (TAU) and Paging messages, which are essential to exchange UE‐specific information with the network. To achieve cost‐efficient resource provisioning, many solutions have been proposed for TAU and Paging management to optimize not only UE experience (ie, battery power consumption) but also network resources (ie, bandwidth). In this paper, we discuss various solution schemes for TAU and Paging in terms of complexity, latency, and computation costs. Also, this review discusses the adverse effects of these solutions on the LTE Key Performance Indicators (KPIs). Furthermore, we present a new trend of MM solutions in LTE networks, called software‐defined network (SDN) and software‐defined virtualization (SDNV). To this end, we examine the existing schemes and challenges in the literature toward next‐generation wireless networks (eg, 5G, Internet‐of‐Things [IoT], and machine to machine [M2M] communications), and we describe user mobility models that are used to analyze the network performance.

Mobility Management for 5G IoT Devices: Improving Power Consumption With Lightweight Signaling Overhead

In mobile wireless networks, mobility management (MM) is an important process to track and locate user equipments (UEs), including Internet-of-Things (IoT) devices, while moving throughout the network. In long term evolution (LTE) and fifth generation (5G) wireless networks, the two MM procedures are known as tracking area update (TAU) and Paging , which are burdensome for both mobile IoT/UEs and network—the IoT/UEs and network always initiate the TAU and Paging , respectively. Because of potentially very high-volume traffic and increasing density of high-mobility IoT/UEs, the TAU/Paging procedure increases the accompanied signaling overhead and the power consumption in the battery-limited IoT/UEs. Hence, this problem will become even worse in 5G because the latter is expected to accommodate exceptional services (e.g., longer IoT/UE battery lifetime). We propose a new solution to solve this problem, named gNB-based UE mobility tracking (gNB-based UeMT). This solution has four features achieving 5G goals. First, the mobile IoT/UEs will no longer trigger the TAU to report their location changes, giving much higher power savings with no signaling overhead. Instead, second, the network elements, gNBs, take over the responsibility of Tracking and Locating these IoT/UEs, giving always-known IoT/UE locations. Third, our Paging procedure is markedly improved over the conventional one, providing very fast IoT/UE reachability with no Paging messages being sent simultaneously. Fourth, this solution guarantees lightweight signaling overhead with very low Paging delay; it achieves about 92% reduction in the corresponding signaling overhead. To this end, our solution adds no implementation complexity; instead, it exploits the already existing LTE/5G communication protocols, functions, and measurement reports.

Low-Latency Communications for Community Resilience Microgrids: A Reinforcement Learning Approach

Machine learning and artificial intelligence (AI) techniques can play a key role in resource allocation and scheduler design in wireless networks that target applications with stringent QoS requirements, such as near real-time control of community resilience microgrids (CRMs). Specifically, for integrated control and communication of multiple CRMs, a large number of microgrid devices need to coexist with traditional mobile user equipments (UEs), which are usually served with self-organized and densified wireless networks with many small cell base stations (SBSs). In such cases, rapid propagation of messages becomes challenging. This calls for a design of efficient resource allocation and user scheduling for delay minimization. In this paper, we introduce a resource allocation algorithm, namely, delay minimization Q-learning (DMQ) scheme, which learns the efficient resource allocation for both the macro cell base stations (eNB) and the SBSs using reinforcement learning at each time-to-transmit interval (TTI). Comparison with the traditional proportional fairness (PF) algorithm and an optimization-based algorithm, namely distributed iterative resource allocation (DIRA) reveals that our scheme can achieve 66% and 33% less latency, respectively. Moreover, DMQ outperforms DIRA, and PF in terms of throughput while achieving the highest fairness.

Analytical characterization of the blockage process in 3GPP New Radio systems with trilateral mobility and multi-connectivity

One of the recent major steps towards 5G cellular systems is standardization of 5G New Radio (NR) operating in the millimeter wave (mmWave) frequency band. This radio access technology (RAT) will potentially provide extraordinary rates at the access interface enabling the set of new bandwidth-greedy applications. However, the blockage of the line-of-sight (LoS) path between 3GPP NR access point (AP) and the user equipment (UE) is known to drastically degrade the performance of the NR communication links thus leading to potential outage conditions. Although the problem of characterizing LoS blockage process has been addressed in the recent literature, the proposed models are mostly limited to stationary locations of APs and UE. In our study, we characterize properties of the LoS blockage process under simultaneous mobility of both blockers and UE. The model is then extended to the cases of Poisson AP deployment, multi-connectivity, and mobility of AP representing ‘trilateral’ (three-sided) mobility model. We also specify a Markov-based model of the blockage process that can be efficiently used in both system level simulations and analytical analysis of 3GPP NR systems. Using this model we demonstrate how to derive various metrics of interest including (i) ,fraction of time in blockage, (ii) SNR and capacity process dynamics, (iii) probability that at time t UE is at the blockage or non-blockage state, (iv) mean and distribution of time to an outage.

A Wideband 28-GHz Transmit–Receive Front-End for 5G Handset Phased Arrays in 40-nm CMOS

This paper reports a transmit-receive (Tx-Rx) front-end (FE) in bulk CMOS targeting fifth-generation (5G) mobile user equipment (UE)-phased arrays. Block-level specifications are first reviewed based on link budget and UE array beam steering considerations, and compared against measured standalone power/low-noise amplifier and phase shifter test circuit performances. Then, the codesign of the Tx-Rx switch and antenna impedance matching is investigated for the important case of wideband dual-resonance networks, leading to a proposed topology that is experimentally verified using silicon passive test structures. A fully integrated FE prototype is fabricated in 1.1-V 1P6M 40-nm bulk CMOS and characterized in detail across digital gain and phase settings. Measurements show Tx-mode P1 dB/PAE1 dB ≥ 14.6 dBm/21.9% across 27-30 GHz, and peak Pout/PAE = 6.5dBm/8.8% at 27 GHz for an 8 x 100-MHz carrier-aggregated 64-QAM OFDM signal. In Rx-mode, a noise figure of 5.5-6 dB and an input-referred third-order intercept point of -8.5 to -6 dBm are achieved across 27-33 GHz. Normalized to area/power consumption, the achieved performance is the state of the art relative to the most recent CMOS/SiGe FEs.

A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things

With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. Partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.

Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks

In heterogeneous ultra-dense networks with millimeter wave macro cells and small cells, base stations (BSs) and mobile user equipments (UEs) perform beamforming operations to establish highly directional links. In spite of the spatial diversity achieved through directional links, as a number of BSs are densely deployed, inter-cell interference caused by concurrent directional transmissions of adjacent BSs becomes severe, resulting in downlink performance degradation in the network. However, it is very difficult to manage inter-cell interference because of the nature of the time-varying wireless fading environment, the dynamic changes in beam propagation directivity, and unpredictable UEs’ locations. In this paper, we propose an online learning-based transmission coordination algorithm based on the framework of multi-armed bandits to learn the unknown characteristics of inter-BS interference and exploit learned data to derive an optimal policy for maximizing the number of successful downlink transmissions. Through the numerical simulations, we verify the effectiveness of the proposed online learning-based inter-BS interference management scheme.

Dynamic AP Clustering and Precoding for User-Centric Virtual Cell Networks

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments.

Enabling Low-Latency Applications in LTE-A Based Mixed Fog/Cloud Computing Systems

In order to enable low-latency computation-intensive applications for mobile user equipments (UEs), computation offloading becomes critical necessary. We tackle the computation offloading problem in a mixed fog and cloud computing system, which is composed of an long term evolution-advanced (LTE-A) small-cell based fog node, a powerful cloud center, and a group of UEs. The optimization problem is formulated into a mixed-integer non-linear programming problem, and through a joint optimization of offloading decision making, computation resource allocation, resource block (RB) assignment, and power distribution, the maximum delay among all the UEs is minimized. Due to its mixed combinatory, we propose a low-complexity iterative suboptimal algorithm called BTFA based joint computation offloading and resource allocation algorithm (FAJORA) to solve it. In FAJORA, first, offloading decisions are obtained via binary tailored fireworks algorithm; then computation resources are allocated by bisection algorithm. Limited by the uplink LTE-A constraints, we allocate feasible RB patterns instead of RBs, and then distribute transmit power among the RBs of each pattern, where Lagrangian dual decomposition is adopted. Since one UE may be allocated with multiple feasible patterns, we propose a novel heuristic algorithm for each UE to extract the optimal pattern from its allocated patterns. Simulation results verify the convergence of the proposed iterative algorithms, and exhibit significant performance gains could be obtained compared with other algorithms.

<italic>DyMo</italic>: Dynamic Monitoring of Large-Scale LTE-Multicast Systems

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, the deployment and management of eMBMS systems are challenging, due to the lack of real-time feedback from the user equipments (UEs). Therefore, we present the Dynamic Monitoring ( DyMo ) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting stochastic group instructions to all UEs . These instructions indicate the reporting rates as a function of the observed quality of service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS signal-to-noise ratio experienced by the $0.1th$ percentile of the UEs with a root mean square error of 0.05% with only 5 to 10 reports per second regardless of the number of UEs.

Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks

Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radio handover and execution results migration when the tasks are off-loaded to FCNs. Furthermore, extra cost, including energy consumption and latency, is generated and affects the revenue of UEs. Previous works rarely consider the mobility of UEs in fog computing networks. In this paper, a generic three-layer fog computing networks architecture is considered, and the mobility of UEs is characterized by the sojourn time in each coverage of FCNs, which follows the exponential distribution. To maximize the revenue of UEs, the off-loading decisions and computation resource allocation are jointly optimized to reduce the probability of migration. The problem is modeled as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. The problem is divided into two parts: tasks off-loading and resource allocation. A Gini coefficient-based FCNs selection algorithm (GCFSA) is proposed to get a sub-optimal off-loading strategy, and a distributed resource optimization algorithm based on genetic algorithm (ROAGA) is implemented to solve the computation resource allocation problem. The proposed algorithms can handle the scenario of UEs' mobility in fog computing networks by significantly reducing the probability of migration. Simulations demonstrate that the proposed algorithms can achieve quasi-optimal revenue performance compared with other baseline algorithms.

Analysis of Impacts of Expected RF EMF Exposure Restrictions on Peak EIRP of 5G User Equipment at 28 GHz and 39 GHz Bands

Above 6 GHz, radio frequency (RF) electromagnetic field (EMF) exposure from the mobile communication user equipment (UE) should be assessed in terms of incident power density, rather than specific absorption rate as below 6 GHz. Such regulatory RF EMF restrictions will constrain the transmit power of the UE and its peak equivalent isotropically radiated power (EIRP). This paper provides an analysis of the peak EIRP levels of UE containing code-book-based beamforming arrays at 28 GHz and 39 GHz. Different types of antenna elements, incremental element spacing, 4- and 8-element array configurations, and realistic housing integration are considered. The analysis and results show that in realistic housing integration, the 3GPP requirements on minimum peak EIRP can be generally met under the expected RF EMF exposure restrictions.

Task Caching, Offloading, and Resource Allocation in D2D-Aided Fog Computing Networks

In this paper, we investigate the allocation of resource in D2D-aided Fog computing system with multiple mobile user equipments (MUEs). We consider each MUE has a request for task from a task library and needs to make a decision on task performing with a selection of three processing modes which include local mode, fog offloading mode, and cloud offloading mode. Two scenarios are considered in this paper, which mean task caching and its optimization in off-peak time, task offloading, and its optimization in immediate time. In particular, task caching refers to cache the completed task application and its related data. In the first scenario, to maximize the average utility of MUEs, a task caching optimization problem is formulated with stochastic theory and is solved by a GA-based task caching algorithm. In the second scenario, to maximize the total utility of system, the task offloading and resource optimization problem is formulated as a mixed integer nonlinear programming problem (MINLP) with a joint consideration of the MUE allocation policy, task offloading policy, and computational resource allocation policy. Due to the nonconvex of the problem, we transform it into multi-MUEs association problem (MMAP) and mixed Fog/Cloud task offloading optimization problem (MFCOOP). The former problem is solved by a Gini coefficient-based MUEs allocation algorithm which can select the most proper MUEs who contribute more to the total utility. The task offloading optimization problem is proved as a potential game and solved by a distributed algorithm with Lagrange multiplier. At last, the simulations show the effectiveness of the proposed scheme with the comparison of other baseline schemes.

Spherical Coverage Characterization of 5G Millimeter Wave User Equipment With 3GPP Specifications

Millimeter-wave (mmWave) frequency bands are promising candidate spectrum for the fifth-generation (5G) mobile communication system, but it requires high directional antenna systems to be applied to the base station and the user equipment (UE) for compensating the high path loss. Due to the randomness of mobile wireless channels, antenna systems in a mobile UE must own a large spherical coverage, which raises new challenges for the performance characterization of 5G mmWave UEs. In the latest specification of the Third-Generation Partnership Project (3GPP), the requirement on UE’s spherical coverage in mmWave frequencies is defined, which is evaluated with the cumulative distribution function of the effective isotropic radiated power. In this paper, the spherical coverage of mmWave UEs is characterized based on the specification of 3GPP, where the impact of device integration, antenna topologies, and user body blockage on the spherical coverage of UE will be analyzed with simulation and measurement results.

An Efficient Computation Offloading Management Scheme in the Densely Deployed Small Cell Networks With Mobile Edge Computing

To tackle the contradiction between the computation intensive applications and the resource-hungry mobile user equipments (UEs), mobile edge computing (MEC) has been provisioned as a promising solution, which enables the UEs to offload the tasks to the MEC servers. Considering the characteristics of small cell networks (SCNs), integrating MEC into SCNs is natural. But in terms of the high interference, multi-access property, and limited resources of small cell base stations (SBSs), an efficient computation offloading scheme is essential. However, there still lack comprehensive studies on this problem in the densely deployed SCNs. In this paper, we study the energy-efficient computation offloading management scheme in the MEC system with SCNs. The aim of this paper is to minimize the energy consumption of all UEs via jointly optimizing computation offloading decision making, spectrum, power, and computation resource allocation. Specially, the UEs need not only to decide whether to offload but also to determine where to offload. First, we present the computation offloading model and formulate this problem as a mix integer non-linear programming problem, which is NP-hard. Taking advantages of genetic algorithm (GA) and particle swarm optimization (PSO), we design a suboptimal algorithm named as hierarchical GA and PSO-based computation algorithm to solve this problem. Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms.