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Enhanced vehicle-to-everything (eV2X) communication is one of the key challenges to be addressed by the fifth generation (5G) of cellular mobile communications. In particular, eV2X includes some 5G vehicular applications targeting fully autonomous driving which require ultra-high reliability. Although vehicular communications are by default assumed between single antennas located on the roof of the transmitter and receiver vehicles, prior art has shown that there are other antenna positions more suitable for V2X communication, depending on the specific communication context. Antenna selection can be used in this case to select one specific antenna or a subset of them better suited for a certain communication link. In this work, we propose a context-aware antenna selection procedure able to enhance the communication with multi-antenna vehicles. To enable such scheme in 5G systems, we discuss the necessary signaling to extend current 5G radio resource control and radio resource management mechanisms, which are mainly focused on single-antenna communication. The signaling overhead caused by context exchange for antenna selection is analyzed and compared to the overhead when reference signals are exchanged for that purpose instead. Finally, simulation results for a 5G platooning use case are presented to show the advantages of antenna selection.

System information broadcasts radio characteristics of the cell, radio characteristics of neighboring cells and alerts. System information consists of a master information block (MIB) message and a series of system information block (SIB) messages. The MIB message is transmitted in the synchronization signal block. The MIB provides the information needed by the mobile to read the physical downlink shared channel. The SIB2 message provides the common information relating to the re‐selection of intra‐/inter‐frequency and inter‐radio access technology cells. The paging procedure consists of transmitting a notification to the mobile in the radio resource control (RRC)_IDLE state or in the RRC_INACTIVE state to inform it of incoming data. Connection reconfiguration messages are used to modify an existing RRC connection. In standby mode, the measurement configuration is broadcast by the gNb base station in the SIB 1 message. The measurements made on the server cell and neighboring cells are used for cell selection and handover.

A BandWidth part (BWP) corresponds to an allocation of resources in the frequency domain. The initial BWP corresponds to a 5G‐NR sub‐band on which the mobile user equipment (UE): searches for the synchronization signal block. The size of the BWP depends on the number of Physical resource blocks and the numerology. The mobile camps on a cell which broadcasts the SSB block and the system information block type 1 (SIB1) information allowing the mobile to find the configuration of the initial BWP partition. The concept of control resource SET (CORESET) was introduced on the 5G‐NR interface to limit the control frequency domain to the capacities of devices. The configuration of the CORESET is transmitted to the mobile UE via Radio Resource Control messages. The initial BWP is transmitted to the mobile by the broadcast information MIB and SIB1.

The data link layer is made up of four sublayers: service data adaptation protocol, packet data convergence protocol (PDCP), radio link control (RLC), and medium access control (MAC). Connectivity to a data network through 5G‐NR radio access and the 5G Core network is provided by a packet data unit session. PDCP is used for radio resource control (RRC) signaling messages, relative to dedicated control data, and for traffic Internet Protocol (IP) packets. The PDCP provides the following functions: the security of RRC traffic and signaling data, and sequence numbering and sequential delivery of RRC messages and IP packets. The IP packet may be subject to the robust header compression header compression to reduce the relative weight of headers. The RLC protocol provides control of the radio link between the mobile and the gNB entity. The MAC protocol defines a header for multiplexing RLC frames from different instances and from MAC protocol control elements.

Modern radio communication services transmit signals from an earth station to a high-altitude station, space station or a space radio system via a feeder link while in Global Systems for Mobile Communication (GSM) and computer networks, the radio uplink transmit from cell phones to base station linking the network core to the communication interphase via an upstream facility. Hitherto, the Single-Carrier Frequency Division Multiple Access (SC-FDMA) has been adopted for uplink access in the Long-Term Evolution (LTE) scheme by the 3GPP. In this journal, the LTE uplink radio resource allocation is addressed as an optimization problem, where the desired solution is the mapping of the schedulable UE to schedulable Resource Blocks (RBs) that maximizes the proportional fairness metric. The particle swarm optimization (PSO) has been employed for this research. PSO is an algorithm that is very easy to implement to solve real time optimization problems and has fewer parameters to adjust when compared to other evolutionary algorithms. The proposed scheme was found to outperform the First Maximum Expansion (FME) and Recursive Maximum Expansion (RME) in terms of simulation time and fairness while maintaining the throughput.

In Press/En prensa In this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) procedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures. To cope with a number of slices devoted to serve various co-deployed tenants, we derive a multi-class queueing model of the network processor. We then present (i) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and (ii) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications. This work is partially supported by the Region of Madrid through the TAPIR-CM project (S2018/TCS-4496), by a Ramon y Cajal grant (ref: RYC-2014-16285) from the Spanish Ministry of Economy and Competitiveness, and by HOME (Hierarchical Open Manufacturing Europe) project supported by the Regione Piemonte, Italia (framework program PORFESR 14/20). This work was supported by the EU Commission through the 5GROWTH project (grant agreement no. 856709).

The 3rd Generation Partnership Project (3GPP) finalized Release 15 specifications for the 5th Generation New Radio (5G NR) in June 2018. In Release 16, the 3GPP worked on not only technical improvements over the previous release but also the introduction of new features. One of the new features is the use of Two-step Random Access Channel (2-step RACH) that enhances 4-step random access with respect to radio resource control connection setup and resume procedures. In this article, we first look into details of 2-step random access defined in the 3GPP Release 16, and briefly introduce recent literature related to 2-step random access. Second, we present challenges of the above random access schemes. Among the challenges, we focus on how a User Equipment (UE) performs self-uplink synchronization with the next-generation Node B (gNB) to resolve preamble collisions, which occur when multiple UEs transmit the same preamble. Specifically, we propose a framework that helps the UE estimate the Timing Advance (TA) command using a deep neural network model and to determine the TA value. Finally, we evaluate the proposed framework in terms of the accuracy of TA command estimation, the inference time, and the battery consumption.

Recently, the number of user equipment with high mobility (such as vehicles) and the high traffic demand is immensely increasing. To sustaining the traffic demand, Time Division Duplex (TDD), which can change the uplink and downlink ratio in the same frequency band, is implemented. However, to satisfy the high traffic demand in 5G vehicular network with limited resources, we need to investigate a more efficient method to adaptively change TDD configuration. In this letter, we propose a reinforcement learning-based resources allocation algorithm that not only considers the multiple parameters of network status but also utilizes them in the learning phase. The contribution of this letter is that our proposal takes into account the future state for resource control through the learning phase. The simulation results show that our proposal outperforms the existing method in throughput and packet loss rate.

The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure, named as NetDataDrilling, provides the dimensioning target area by means of network data selection and visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost. The proposed HRND is based on the novel quality of experience (QoE) parameter ζ by probabilistically characterizing the 5G radio resource control (rrc) states to ensure the availability of peak data rates for the MNO's required percentage of the time. The simulation results show the fulfilment of QoE and load balancing parameters with significant cost savings compared to the conventional RND methodology. This work was supported by the Spanish National Project TERESA-ADA (MINECO/AEI/R, UE), under Grant TEC2017-90093-C3-2-R.

Massive Multiple Input Multiple Output (MIMO) is an essential component for future wireless cellular networks. One of its biggest advantages is to use the 5G spectrum more intelligently by extending both coverage (via high gain adaptive beamforming) and capacity (via high order spatial multiplexing). In this paper, we evaluate the performance of Time-division duplex (TDD)-based massive MIMO deployment scenario in one of the commercial sites in Turkey. Our experimental results reveal three major contributions: (i) TDD-based massive MIMO in 10 Mhz reveals up to 212% and 50% higher cell throughput compared to Frequency-division duplex (FDD)-based MIMO deployments with 10 Mhz and 20 Mhz respectively. The Downlink (DL) throughput is also observed to be better in mid/far points. (ii) Together with the usage of TDD-based massive MIMO inside the same commercial site, median values of total cell traffic, Uplink (UL) Spectral Efficiency (SE) and DL schedule Transmission Time Interval (TTI) duty cycle have improved 38%, 9% and 14.5% compared to FDD-based MIMO scenario respectively. (iii) Finally, we address some of the challenges of the massive MIMO deployments and the possible trade-offs that can be observed in terms of Radio Resource Control (RRC)-connected User Equipments (UEs), cell throughput, available Sounding Reference Signal (SRS) resources and pairing opportunities provided by massive MIMO.

Motivated by the rapid deployment of 5G, we carry out an in-depth measurement study of the performance, power consumption, and application quality-of-experience (QoE) of commercial 5G networks in the wild. We examine different 5G carriers, deployment schemes (Non-Standalone, NSA vs. Standalone, SA), radio bands (mmWave and sub 6-GHz), protocol configurations (_e.g._ Radio Resource Control state transitions), mobility patterns (stationary, walking, driving), client devices (_i.e._ User Equipment), and upper-layer applications (file download, video streaming, and web browsing). Our findings reveal key characteristics of commercial 5G in terms of throughput, latency, handover behaviors, radio state transitions, and radio power consumption under the above diverse scenarios, with detailed comparisons to 4G/LTE networks. Furthermore, our study provides key insights into how upper-layer applications should best utilize 5G by balancing the critical tradeoff between performance and energy consumption, as well as by taking into account the availability of both network and computation resources. We have released the datasets and tools of our study at https://github.com/SIGCOMM21-5G/artifact.

Telecom networks together with mobile phones must be rigorously tested for robustness against vulnerabilities in order to guarantee availability. RRC protocol is responsible for the management of radio resources and is among the most important telecom protocols whose extensive testing is warranted. To that end, we present a novel RRC fuzzer, called Berserker, for 4G and 5G. Berserker's novelty comes from being backward and forward compatible to any version of 4G and 5G RRC technical specifications. It is based on RRC message format definitions in ASN.1 and additionally covers fuzz testing of another protocol, called NAS, tunneled in RRC. Berserker uses concrete implementations of telecom protocol stack and is unaffected by lower layer protocol handlings like encryption and segmentation. It is also capable of evading size and type constraints in RRC message format definitions. Berserker discovered two previously unknown serious vulnerabilities in srsLTE -- one of which also affects openLTE -- confirming its applicability to telecom robustness., 19 pages, 9 figures, 17 tables

This poster presents the design, development and test results of an energy consumption analysis module developed over ns3 Millimeter Wave (mmWave) communication for analyzing power consumption for 5G New Radio (NR) User Equipment (UE) during both continuous and discontinuous packet receptions. This module is important to analyze and explore the energy consumption behavior of the 5G communication protocols under the NR technology. The developed module includes the complete Radio Resource Control (RRC) state machine for 5G NR recommended by 3GPP Specification 38.840. To the best of our knowledge, the designed module is the first of its kind that provides a comprehensive energy analysis for the 5G NR UEs over mmWave communication.

The network Quality of Service (QoS) metrics such as the access time, the bandwidth, and the packet loss play an important role in determining the Quality of Experience (QoE) of mobile applications. Various factors like the Radio Resource Control (RRC) states, the Mobile Network Operator (MNO) specific retransmission configurations, handovers triggered by the user mobility, the network load, etc. can cause high variability in these QoS metrics on 4G/LTE, and WiFi networks, which can be detrimental to the application QoE. Therefore, exposing the mobile application to realistic network QoS metrics is critical for a tester attempting to predict its QoE. A viable approach is testing using synthetic traces. The main challenge in the generation of realistic synthetic traces is the diversity of environments and the lack of wide scope of real traces to calibrate the generators. In this paper, we describe a measurement-driven methodology based on transfer learning with Long Short Term Memory (LSTM) neural nets to solve this problem. The methodology requires a relatively short sample of the targeted environment to adapt the presented basic model to new environments, thus simplifying synthetic traces generation. We present this feature for realistic WiFi and LTE cloud access time models adapted for diverse target environments with a trace size of just 6000 samples measured over a few tens of minutes. We demonstrate that synthetic traces generated from these models are capable of accurately reproducing application QoE metric distributions including their outlier values., Accepted by Elsevier Computer Communications, 24 pages, 14 figures, corrected typos, figures in grayscale

Flexible Factory Partner Alliance (FFPA) has defined the technical specifications for Smart Resource Flow (SRF) Wireless Platform which ensures stable communications in an environment where various wireless systems coexist. SRF Wireless Platform is based on the Smart Resource Flow approach proposed by National Institute of Information and Communications Technology (NICT), which is a multi-layer whole-system engineering approach to managing manufacturing resources to achieve optimal system performance. Authors have been taking part in FFPA activities and also have been doing research and development on wireless systems based on SRF Wireless Platform. In this paper, authors propose radio resource control technologies for SRF Wireless Platform. The results of evaluation tests clarify effectiveness of the technologies for practical use.

With the evolution of WCN (Wireless communication networks), the absolute fulfillment of security occupies the fundamental concern. In view of security, we have identified another research direction based on the attenuation impact of rain in WCN. An approach is initiated by an eavesdropper in which a secure communication environment is degraded by generating Artificial Rain (AR), which creates an abatement in the secrecy rate, and the cybersecurity gets compromised. By doing so, an attacking scenario is perceived, in which an intruder models a Half-Duplex (HD) attack. Half-Duplex specifies the attack on the downlink instead of targeting both uplink and downlink. This allows the attacker to alleviate the miss-rate of the attacking attempts. The layout for the HD attack is explained using RRC (Radio Resource Control)-setup. Further, we have determined and examined the performance parameters such as secrecy rate, energy efficiency, miss-rate, sensitivity in the presence of AR. Further comparison of rural and urban scenarios in the presence and absence of AR is carried out concerning the variation in secrecy rate with respect to the millimeter-wave frequencies and distance. Lastly, the methodology of the HD attack is simulated, revealing that the HD attack maintains a low miss rate with improved performance as compared to the performance and miss-rate attained by the full-duplex attack

Faced with the growth of massive data in the 5G network and the low query efficiency in the existing signaling synthesis algorithm, the traditional LTE/LTE-A signaling monitor scheme has been unable to satisfy with the new 5G network architecture. The Radio Resource Control (RRC) protocol is the core of the control plane, which manages and controls the wireless resources of the network. Based on this, a signaling monitor scheme of RRC protocol suitable for 5G road tester was proposed and the specific functions of its main submodule were introduced in detail. Additionally, this paper discussed an improved dynamic hash signaling synthesis algorithm based on AVL tree under a new network architecture. The tree structure is used to reduce the query time of traditional algorithm in the hash table, so as to quickly deal with hash collisions and improve the real time on the signaling synthesis. At present, the proposed scheme was applied to the real network testing of air interface data in the 5G road tester. The experimental results show that the improved algorithm can efficiently solve the issue of low efficiency of Call Detail Recording (CDR) synthesis and the average query time can be reduced by 49.3% and 33.1% compared with the two traditional algorithms. The signaling synthesis scheme described above achieves the expected effect and signaling messages in RRC protocol can be accurately decoded and monitored in real time.

Narrowband Internet of Things (NB-IoT) is becoming one of the most promising low power wide area (LPWA) networking technologies. It can support more than 50 000 devices within a cell using licensed spectrum. NB-IoT provides low energy consumption, reliable connectivity and deep indoor coverage for the device, making it a good candidate for IoT use cases. NB-IoT introduces two novel energy-saving techniques, namely extended discontinuous reception (eDRX) and power saving mode (PSM). This paper presents a Markov chain model to evaluate the power consumption and latency of NB-IoT devices using PSM and eDRX. By exploiting the characteristics of the steady-state distribution of the Markov chain, the probabilities in steady-state can be obtained explicitly. Based on these probabilities, we calculate the system downlink (DL) latency as a function of different timers of these power-saving features. We also compare the model to simulation results obtained from the ns-3 event-based network simulator, to determine its accuracy. The results show that its performance in terms of energy and latency is comparable. Our model is accurate with consideration of the protocol details and the new Radio Resource Control (RRC) Idle features of NB-IoT. The results show that the analytical model achieves an average accuracy of more than 91% for power consumption and DL latency. Lastly, we use the model to automatically determine the optimal parameter set in terms of latency and power consumption for various IoT use cases with different traffic requirements, based on multi-objective analysis of the Pareto front.

NarrowBand Internet of Things (NB-IoT) is an emerging cellular IoT technology that offers attractive features for deploying low-power wide-area networks suitable for implementing massive machine-type communications. NB-IoT features include, e.g., extended coverage and deep penetration for massive connectivity, longer battery-life, appropriate throughput, and desired latency at lower bandwidth. Regarding the device energy consumption, NB-IoT is mostly underestimated for its control and signaling overheads, which calls for a better understanding of the energy consumption profiling of an NB-IoT radio transceiver. With this aim, this work presents a thorough investigation of the energy consumption profiling of the radio resource control (RRC) communication protocol between an NB-IoT radio transceiver and a cellular base station. Using two different commercial off the shelf NB-IoT boards and two mobile network operators (MNOs) NB-IoT test networks operational at Tallinn University of Technology, Estonia, we propose an empirical baseline energy consumption model. Based on comprehensive analyses of the profile traces from the widely used BG96 NB-IoT module operating in various states of the RRC protocol, our results indicate that the proposed model accurately depicts the baseline energy consumption of an NB-IoT radio transceiver while operating at different coverage class levels. The evaluation errors of our proposed model vary between 0.33% and 15.38%.

With the advent of 5th generation (5G) technology, the mobile paradigm witnesses a tremendous evolution involving the development of a plethora of new applications and services. This enormous technological growth is accompanied with an huge signaling overhead among 5G network elements, especially with emergence of massive devices connectivity. This heavy signaling load will certainly be associated with an important security threats landscape, including denial of service (DoS) attacks against the 5G control plane. In this paper, we analyse the performance of a defense mechanism based randomization technique designed to mitigate the impact of DoS signaling attack in 5G system. Based on massive machine-type communications (mMTC) traffic pattern, the simulation results show that the proposed randomization mechanism decreases significantly the signaling data volume raised from the new 5G Radio Resource Control (RRC) model under normal and malicious operating conditions, which up to 70% while avoiding the unnecessary resource consumption.

Communication system is evolving day by day and now entire globe communication is based on internet. Office work, academic activities in engineering field, business, banking system, government offices and different digital gazettes are performing regular processes using internet. So, data traffic volume goes on increasing exponentially and needs high capacity networks. 5th Generation (5G) networks are optimistic system to accommodate requirements of increasing demand. Complexity and challenges will also increase as system grows. Hence imminent insights and predictions are important to govern the 5G system systematically and avoid disasters in the coming days. Computational intelligence techniques are required to forecast solutions to run the system smoothly. Fuzzy logic controller is one such system to handle the dynamics and the non-linearity processes in 5G network. Authors propose novel fuzzy control algorithm to allocate the resources for User Equipment (UE) in 5G wireless network. Nowadays in the internet, multimedia data is the preferred traffic and that has contributed more to the volume of data. So Real time (RT) and Non Real time (NRT) types of requests are considered in this research. Radio Resource Control (RRC) connection management and communication protocol is used by wireless base station gNodeB (gNB) of 5G networks. The function of RRC is controlled by proposed fuzzy control algorithm. Fuzzy rules are designed, applied and analyzed for different combinations of inputs and outputs. Proposed RT First queue scheduling is used to process the RT and NRT requests. It is compared with First Come First Serve (FCFS) scheduling. RT First queue scheduling shows average response time 40% less than FCFS.

This paper describes the implementation of 5G New Radio (NR) network monitoring equipment using software defined radio (SDR). Network operation monitoring is performed based on the radio resource control (RRC) message and control channel analysis. The proposed measurement equipment is capable of synchronization with available network and performing control channel information interpretation and analysis. In order to validate the operation of the proposed monitoring equipment, the measurement results have been compared with the commercially available diagnostic monitor tool, which can directly access data from a smartphone chipset. The performed measurements validate correct operation of the proposed measurement equipment.

The Fifth-Generation (5G) cellular mobile networks are expected to support three service categories and one of them is Ultra-reliable low latency communication (URRLC). For URLLC type of services, 3GPP has strict performance requirements of low latency and high reliability. According to ITU-R M.2410-0 and 3GPP Technical Specification (TS) 38.913, error rate should be lower than 1 packet loss in 105 packets and latencies should be lower than 0.5ms uplink/downlink for URLLC. To ensure such high reliability, packet retransmissions or packet duplication may be performed which will incorporate additional delays to the communication. Hence, there are multiple open research problems for achieving very low latency along with high reliability for URLLC. To the best of our knowledge, this paper for first time solved one of the research problems, by proposing a new call flow where 5G Radio Resource Control (RRC) Release message is to be enhanced such that accessing network for Mobile Originating (MO) call/data in ultra-reliable low latency devices will achieve a reliability with lower than error rate of 1 packet loss in 105 packets along with shorter latency than existing art. Through extensive experiments and simulation, the proposed idea, reduced error rate below than 0.0001% with improved latency as success rate increased to 99.99% during initial access than existing method.

To meet the low latency demand of 5G Ultra-Reliable and Low Latency Communications (URLLC), International Telecommunication Union (ITU) specifically required the control plane (CP) latency, i.e., the time for user equipment (UE) to change from Radio Resource Control (RRC)_IDLE state to RRC_CONNECTED state, to be less than 20 ms and also encouraged proposals to further reduce it to less than 10 ms. To fulfill the CP latency target, a novel random access procedure (RAP) design is introduced, in which the message exchange procedure is simplified and the demodulation reference signal (DMRS) is used to represent the UE ID. Simulation results show that each UE completes the RAP in one iteration of the proposed RAP due to its collision-free property. Specifically, with the pro-posed RAP, at least 85% of URLLC users complete the RAP within 10 ms in the worst case.

Recently, deep learning has emerged as a state-of-the-art machine learning technique with promising potential to drive significant breakthroughs in a wide range of research areas. The application of deep learning for network traffic control, however, remains immature due to the difficulty in uniquely characterizing the network traffic features as an appropriate input and output dataset to the learning structures. The network traffic features are anticipated to be even more dynamic and complex in the UDNs of the emerging 5G networks with high traffi c demands coupled with beamforming and massive MIMO technologies. Therefore, it is critical for 5G network operators to carry out radio resource control in an efficient manner instead of adopting the simple conventional F/TDD. This is because the conventional uplink-downlink configuration change in the existing dynamic TDD method, typically used for resource assignment in beamforming and massive-MIMO-based UDNs, is prone to repeated congestion. In this article, we address this issue and discuss how to leverage the deep LSTM learning technique to make localized prediction of the traffic load at the UDN base station (i.e., the eNB). Based on localized prediction, our proposed algorithm executes the appropriate action policy a priori to avoid/alleviate the congestion in an intelligent fashion. Simulation results demonstrate that our proposal outperforms the conventional method in terms of packet loss rate, throughput, and MOS.

In long-term evolution-advanced (LTE-A) network, the third-generation partnership project has proposed machine-type communications (MTC) as a new paradigm where devices transfer data among themselves with limited human interaction. For devices in MTC, power saving has become an important issue because many devices are powered by battery. In LTE-A network, the standard power saving mechanism, called discontinuous reception (DRX), is designed for normal mobile users, not for MTC devices. In this paper, we propose an optimistic DRX (ODRX) mechanism to be suitable for MTC devices. ODRX considers the radio resource control connection release and re-establishment to save more power. We also introduce the optimistic flag to allow longer sleep periods. Analytical and simulation models are proposed to investigate the performance of ODRX, and ODRX is then compared with the standard DRX and dynamic DRX (DDRX) through simulation experiments. The results show that ODRX outperforms standard DRX and DDRX by gaining significant extra power saving with little extra wake up latency. We also propose guidelines to configure ODRX parameters.

Broadband mobile networks utilize a radio resource control (RRC) state machine to allocate scarce radio resources. Current implementations introduce high latencies and cross-layer degradation. Recently, the RRC enhancements, continuous packet connectivity (CPC), and the enhanced forward access channel (Enhanced FACH), have emerged in UMTS. We measure the availability and performance of these enhancements on a network serving a market with a population in the millions. We demonstrate that these enhancements offer significant reductions in latency, mobile device energy consumption, and improved end user experience. We develop new over-the-air measurements that resolve existing limitations in measuring RRC parameters. We find CPC provides significant benefits with minimal resource costs, prompting us to rethink past optimization strategies. We examine the cross-layer performance of CPC and Enhanced FACH, concluding that CPC provides reductions in mobile device energy consumption for many applications. While the performance increase of HS-FACH is substantial, cross-layer performance is limited by the legacy uplink random access channel (RACH), and we conclude full support of Enhanced FACH is necessary to benefit most applications. Given that UMTS growth will exceed LTE for several more years and the greater worldwide deployment of UMTS, our quantitative results should be of great interest to network operators adding capacity to these networks. Finally, these results provide new insights for application developers wishing to optimize performance with these RRC enhancements.

In this paper, we consider the problem of joint user scheduling and dynamic pilot allocation in a Time-Division Duplex (TDD) based Massive MIMO network under varying traffic condition. One of the main problems with Massive MIMO systems is that the number of available orthogonal pilot signals is limited, and the dynamic allocation of these signals to different users is crucially needed to utilize the full benefit of these systems. In addition, pilot signals are radio resource control (RRC) configured in practice, and hence the frequent reconfiguration causes high signaling overhead and is costly. Using Lyapunov optimization framework, we develop an optimal algorithm that first assigns pilots dynamically based on queue sizes and the channel conditions of users as well as the reconfiguration cost at large time-scale. Then, it schedules users on a small-time scale. Numerical results show the efficacy of our algorithm and demonstrate that pilots do not need to be configured frequently at the expense of increased queue delay., Comment: submitted

With the rapid increase in the number of mobile users, wireless access technologies are evolving to provide mobile users with high data rates and support new applications that include both human and machine-type communications. Heterogeneous networks (HetNets), created by the joint installation of macro cells and a large number of densely deployed small cells, are considered an important solution to deal with the increasing network capacity demands and provide high coverage to wireless users in future fifth generation (5G) wireless networks. Due to the increasing complexity of network topology in 5G HetNets with the integration of many different base station types, in 5G architecture mobility management has many challenges. Intense deployment of small cells, along with many advantages it provides, brings important mobility management problems such as frequent handover (HO), HO failure, HO delays, ping-pong HO and high energy consumption which will result in lower user experience and heavy signal loads. In this paper, we provide a comprehensive study on the mobility management in 5G HetNet in terms of radio resource control, the initial access and registration procedure of the user equipment (UE) to the network, the paging procedure that provides the location of the UE within the network, connected mode mobility management schemes, beam level mobility and beam management. Besides, this paper addresses the challenges and suggest possible solutions for the 5G mobility management.

In Fifth Generation (5G), wireless cellular networks, smartphone battery efficiency, and optimal utilization of power have become a matter of utmost importance. Battery and power are an area of significant challenges considering smartphones these days are equipped with advanced technological network features and systems. These features require much simultaneous power to make decisions and to transfer information between devices and network to provide best the user experience. Furthermore, to meet the demands of increased data capacity, data rate, and to provide the best quality of service, there is a need to adopt energy-efficient architectures. This paper presents system-level architectural changes on both User Equipment (UE) and Network elements along with a proposal to modify control signaling as part of Radio Resource Control messages using smartphone battery level. Additionally, we presented real-world 5G mmWave field results, showing impacts on device battery life in varying RF conditions and proposed methods to allocate optimal network resources and improve the energy efficiency by modifying radio layer parameters between devices and base stations. Without these proposed architecture level and system-level algorithm changes, realizing optimal and consistent 5G speeds will be near impossible.