Reduce Signaling Overhead Research Articles

Design Optimization of Tracking Area List in Lte Using 2D Markov Model

In LTE there is a logical grouping of cells called Tracking Area (TA) and TAs are further grouped into Tracking Area List (TAL). Signaling overhead is greatly affected by the size of the TA and TAL respectively. Designing an optimum TAL would greatly reduce signaling overhead resulting from Tracking Area Update (TAU) and Paging procedures, which in return maximizes the network performance. This paper adopts a 2D Markov model that can be used for design optimization of TAL in LTE system by estimating the number of users in a cell within a time slot and the probability of the next location they might move to, as users move from and into cells periodically. The model was simulated in Matlab simulation software. The 2D Markov model was used to calculate TAU overhead, paging overhead and the total signaling overheads. The numerical results show that our model probably reduces the signaling overhead by about an average of 56% than that of the conventional TA scheme.

Anchor-Based Active Set for User-Centric Multi-Connectivity: Mobility Enhancement and Performance Evaluation

By organizing a dynamic transmission point group, user-centric networks (UCNs) are considered as a feasible solution to mine the potentials of dense networks where the densification gain is blocked by strong interference and frequent handovers. However, the state-of-art studies mostly focus on improving throughput performance. In this paper, user-centric multi-connectivity architecture is proposed, where multiple small cells serve the user as a user-specific active set to reduce the number of radio link failures (RLFs) and to increase the throughput gain, especially for cell-edge users. Especially, master eNodeB (MeNB) is chosen in the active set as a mobility anchor to control its neighboring small cells that are termed as slave eNodeBs (SeNBs), which reduces signaling overheads during handovers. To provide user-centric service, the active set follows the user's trajectory by adding new SeNB that provides strong signal strength and releasing serving SeNB that is far away from the user. Moreover, the design of key mobility management procedures for active set forming/reforming is taken into consideration. The results show that compared with single connectivity, the RLF in the proposed architecture shows a decrease of more than 50% when user speed is 3 km/h, and the average user throughput achieves an increase of around 74% gains when TP density is 2000 TPs/km 2 .

Agglomerative User Clustering and Cluster Scheduling for FDD Massive MIMO Systems

Two-stage precoding is a promising transmission strategy for frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its large multiplexing gain with significant overhead reduction in both downlink training and feedback. In this paper, we propose a new agglomerative clustering method to significantly simplify the user clustering process. In order to suppress the residual inter-cluster interference in realistic two-stage precoding transmission, we propose an average signal-to-leakage-plus-noise ratio (SLNR)-based iterative cluster scheduling and outer precoder design scheme to achieve a balance between providing high multiplexing gain and improving per-user rate. For uniform linear arrays (ULAs), a fast implementation of the scheme with discrete Fourier transformation (DFT) approximation is proposed. The numerical results demonstrate the performance improvement of the proposed methods over the existing methods.

RRC State Handling for 5G

Standardization work for the 5th Generation of mobile communications (5G) is under way in the 3rd Generation Partnership Project (3GPP). The design of the user equipment (UE) state machine is one of the central questions related to the overall control plane design. In this article, we present a state machine for 5G that consists of a novel Radio Resource Control (RRC) Connected Inactive state in addition to the conventional RRC Idle and RRC Connected states. In RRC Connected Inactive, the UE Context is stored in the UE and in the network, and the connection between the Radio Access Network (RAN) and the Core Network (CN) is kept active to minimize control plane latency and UE power consumption during state transition. In addition, RRC Connected Inactive is configurable so that requirements of diverse 5G use cases can be fulfilled without increasing the overall number of RRC states. Performance analysis shows that RRC Connected Inactive can achieve up to 8x latency improvement, 5x power efficiency and 3.5x signaling overhead reduction, when compared to Long Term Evolution (LTE) RRC Idle.

Q-FRPML: QoS-Centric Fault-Resilient Routing Protocol for Mobile-WSN Based Low Power Lossy Networks

Considering the significance of mobile-Wireless Sensor Networks (WSNs) under Low Power Lossy Network (LLN) Conditions, in this paper a highly robust and QoS-Centric Fault-Resilient Routing Protocol for Mobile-WSN in LLN (Q-FRPML) has been proposed. Unlike classical routing approaches such as Routing Protocol for 6LowPAN based LLNs (RPL), our proposed Q-FRPML protocol contributes multiple novelties including received signal strength indicator (RSSI) based mobile node positioning for fault-resilient communication, proactive node management, RSSI and ETX objective functions based best parent node selection, link layer adaptive fault-resilient alternate path formation for QoS centric communication over mobile-WSNs. Q-FRPML protocol is implemented in parallel to the link layer of the classical RPL IEEE 802.15.4 protocol stack that once detecting any link-outage executes best parent node selection and alternate path formation to assure reliable data delivery. In this process, Q-FRPML avoids continuous network discovery that significantly reduces signaling overheads and energy consumption. Contiki-Cooja based simulation results have revealed that the proposed Q-FRPML protocol outperforms state-of-art native RPL or S-RPL protocol in terms of higher packet delivery ratio, lower packet loss ratio and end-to-end delay under varying network or load conditions. Though, Q-FRPML protocol has been applied in parallel to the native RPL, it preserves backward compatibility and hence can be applied in real-time mobile-WSN based QoS centric communication purposes.

Analysis of a NEMO enabled PMIPv6 based Mobility Support for an Efficient Information Transmission

Nowadays, wireless sensor networks (WSNs) have been widely adopted in structural health monitoring (SHM) systems for social overhead capital (SOC) public infrastructures. Structural health information, environmental disturbances and sudden changes of weather conditions, damage detections, and external load quantizing are among the capabilities required of SHM systems. These information requires an efficient transmission with which an efficient mobility management support for wireless networks can provide. This paper deals with the analysis of mobility management schemes in order to address the real-time requirement of data traffic delivery for critical SHM information. The host-based and network-based mobility management protocols have been identified and the advantages of network mobility (NEMO) enabled Proxy Mobile Internet Protocol version 6 (PMIPv6) have been leveraged in order to address the SHM information transmission needs. The scheme allows an efficient information transmission as it improves the handover performance due to shortened handover latency as well as reduced signaling overhead.

Block-Sparsity-Based Multiuser Detection for Uplink Grant-Free NOMA

Grant-free non-orthogonal multiple access has recently gained significant attention for reducing signaling overhead in machine-type communications. In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame-based multi-user uplink scenarios where users are (in)-active for the duration of a frame, namely, the frame-wise joint sparsity model. First, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and considering the user sparsity level to be unknown for multiuser detection, two enhanced BCS-based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross-validation aided block sparsity adaptive subspace pursuit (CVA-BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance by reasonably setting the threshold based on the additive white Gaussian noise floor. Moreover, the proposed CVA-BSASP algorithm is a highly practical algorithm design that adopts the statistical and machine learning mechanism cross-validation to determine the stopping condition of the algorithm and this does not require prior knowledge. Furthermore, the convergence and the computational complexity of the proposed algorithms are derived and the superior performance of the proposed algorithms is demonstrated by numerical experiments.

Joint Channel Estimation and Multiuser Detection for Uplink Grant-Free NOMA

Grant-free non-orthogonal multiple access is an emerging research topic in machine-type communications, which is used to reduce signaling overhead. In this context, this letter introduces a novel joint channel estimation (CE) and multiuser detection (MUD) framework for the frame based multi-user transmission scenario where users are (in)active for the duration of a frame. First, considering the inherent frame-wise joint sparsity of the pilot and data phases in the entire frame, we formulate the multiple measurement vector-compressive sensing (MMV-CS) framework. Then, transfer the MMV-CS to a block-sparse single measurement vector-CS (BS-SMV-CS) model. Finally, to make explicit use of the block sparsity inherent in the BS-SMV-CS model and consider that the user sparsity level should be unknown for receiver, an enhanced subspace pursuit (SP) algorithm is developed, i.e., block sparsity adaptive SP. Superior performance of the proposed joint CE and MUD framework is demonstrated by simulation results.

On Efficient Scheduling of H2H Traffic and Reducing Signaling Overhead due to Uplink Small Data M2M Traffic in LTE-A Networks

Large coverage and global connectivity makes cellular networks as preferred choice for internet of things (IoT). Machine-to-machine (M2M) communications deal with communication and networking aspects of IoT. Since, cellular networks are optimized to support human-to-human (H2H) communication (e.g., Voice calls, Internet), incorporating M2M communication may affect the QoS of the former. Also, the large number of M2M devices incur significant signaling overhead on both core network (CN) and radio access network (RAN). In LTE-A networks, EPS bearer establishment procedure to connect a device to the PDN gateway involves several signaling messages exchange between the device and the network. M2M devices mostly generate traffic of low volume and less frequent, in nature. So, it is very uneconomical to have rigorous signaling messages exchange to send few bytes of data. In this paper, we first studied class based dynamic priority (CBDP) algorithm Giluka et al. (in: Proceedings of IEEE WF-IoT, 2014), which is a delay aware radio resource scheduling algorithm to support uplink M2M traffic with minimal effect on QoS of uplink H2H traffic. Further, we modeled the optimal behavior of the CBDP algorithm and compared with its behavior in practical scenarios. Apart from this, we propose a lightweight EPS bearer establishment procedure to be followed by M2M devices sending small data, in which M2M small data is piggybacked with control message. Further, in the same procedure, redundant signaling messages for small data transmission (SDT) are carefully removed preserving the security aspects of the system. To ensure security for the small data transmitted, a new insightful technique of replacing authentication with confidentiality is conceived. With this, we propose an enhanced version of CBDP algorithm, named as Non-SDT-CBDP algorithm or NSDT-CBDP algorithm, which schedules resources only to H2H and NSDT-M2M flows while SDT-M2M flows are piggybacked with Message 3 (MSG-3) of lightweight EPS bearer establishment procedure. The simulation results show performance gain of NSDT-CBDP over CBDP, specially for class-3 M2M and class-4 H2H. The NSDT-CBDP algorithm show percentage reduction in packet loss ratio by 25% for class-3 M2M, percentage reduction in end-to-end delay by 11 and 19% for class-4 H2H and class-3 M2M, percentage gain in throughput by 27 and 19% for class-4 H2H and class-3 M2M. Apart from this, NSDT-CBDP algorithm is able to allocate 12% more RBs to H2H devices in comparison to CBDP algorithm.

Decoupling 5G Network Control: Centralised Coordination and Distributed Adaptation

Fifth generation mobile networks (5G) will be featured by miniaturised cells and massive dense deployment. Traditional centralised network control cannot adapt to high signalling delay, and is therefore not scalable for future 5G networks.To address this issue, we adopt the software-defined networking (SDN) approach of decoupled network control and data transmission. In particular, delay-sensitive interference suppression for data transmission is decoupled from delay-tolerant topology control and base station coordination. This substantially alleviates the requirement of network control on delay and complexity, hence simplifying 5G control plane design, reducing signalling overhead, and enhancing network scalability. Case studies show that our decoupled network control is effective for timely interference mitigation and reliable topology management. The stability and scalability of our approach are also demonstrated.

A Hybrid Routing Protocol for Wireless Distributed Networks

Trends in networking are shifting from distributed to logically centralized networks. Software defined networking (SDN) is the key technology behind this shift. This new paradigm not only proves the ease of network management in wired networks but also results into graceful evolution of networking protocols. Moreover, wireless distributed networks (WDNs) and software defined radios (SDRs) are also shifting towards the centralized approach. However, due to distributed control nature of WDNs, already prevailing routing algorithms cannot cope the design principle of centralized routing algorithms. Hence, a new routing algorithm that incorporates SDN in WDNs is required. In this paper, we propose a logically centralized approach for WDNs called centralized approach to mobile ad hoc network (CATMAN). It is a self-healing and hybrid routing protocol. It combines distributed and logically centralized network control along with the flavors of reactive, proactive, and opportunistic routing protocols. CATMAN is designed to automatically switch between logically centralized routing protocol and distributed routing protocol based on the types of available network nodes. The protocol significantly reduces control overhead and hence improves bandwidth utilization. While route computations, this protocol deliberately avoids the nodes with weaker batteries, optimizing the effective life of whole network. The simulation results showed that the CATMAN protocol outperform better approach to mobile ad hoc network (BATMAN) protocol by signaling overhead reduction up to 570%.

WITHDRAWN: Low signaling overhead group-based power-control scheme for massive machine-type communications

Abstract Next-generation (5G) cellular networks must deal with massive Machine-Type Communications (MTC) for Internet of Things applications. Because of the potentially large number of coexisting MTC devices, achieving efficient device management and signaling is an essential issue. Moreover, many MTC devices are battery-operated and require a long battery life. It is difficult to achieve low power consumption using MTC devices with limited control signaling overhead. To accomplish this, this study proposes a group-based power-control scheme. To reduce signaling overhead, the proposed scheme assigns one ID and one power-control parameter to a group of MTC links that are allocated on the same resource block. To maintain low power consumption, we formulate a resource allocation problem that aims to minimize the power consumed by MTC links. We propose a greedy algorithm to solve this problem. Numerical results show that the proposed scheme can achieve low power consumption and low signaling overhead.

PoC of SCMA-Based Uplink Grant-Free Transmission in UCNC for 5G

A 5G mobile network has evolved from a cell centric radio access network to a user centric one. A user-centric no-cell (UCNC) framework has been proposed, which enables physical layer coordination over a large number of transmission and reception points for each mobile user to always experience cell-center-like communications. This paper presents some key technologies in the UCNC framework. The focus is on proof of concept of UL grant-free transmissions in the UCNC architecture. Massive connectivity supporting a huge number of devices is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant-free transmission based on sparse code multiple access (SCMA) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectivity requirements. Grant-free transmission conducted in RRC connected state can reduce signaling overhead, while in energy conserved operation (ECO) state, it can significantly reduce the user plane transmission latency. Extensive laboratory testing has been conducted in a collaborative project of Huawei and Telefonica to verify the performance matrices. Taking LTE as a baseline for comparison purposes, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with the SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.

Impact of Imperfect Source-to-Relay CSI in Amplify-and-Forward Relay Networks

Cooperative communication assisted by relays is regarded as an effective means of achieving spatial diversity. The achievable diversity gain relies on the channel-state information (CSI) that can be acquired at the destination because spatial diversity combining unavoidably requires the disintegrated channel information. To reduce signaling overhead, quantization and compression are applied prior to the delivery of the source-to-relay (SR) CSI to the destination. This paper studies an amplify-and-forward relay network while considering the impact of the quantization of the SR CSI. Several quantization methods relying on the statistics of the SR CSI are investigated. The probability density functions of upper and lower bounds on the signal-to-noise ratio (SNR) and the corresponding symbol-error rate (SER) achieved at the destination are derived analytically. Monte Carlo simulations are performed to compare the simulation results with the analytical results of the SER bounds and to verify the effectiveness of the proposed channel quantizer. A combined method of determining the quantization interval based on equal-probability partitioning and determining the representative quanta based on the minimum-mean-square-error criterion is proposed and is verified to have the lowest quantization error among the noniterative quantization methods compared here. It is shown via the simulations that when using an iterative procedure based on the Lloyd–Max procedure, three bits per dimension are sufficient to achieve negligible SNR degradation.

A New Mobility Management Scheme for Intelligent Transportation Systems

Internet of Things (IoT) applications attract great interests recently. Intelligent Transportation System (ITS) is one of the most appealing applications of IoT. It is essential to efficiently managing various mobility in IoT/ITS to provide seamless Internet connectivity. The existing mobility management proposals are needed to be extended to support diverse mobility in future IoT/ITS environment. Quite a few distinct features of the mobility presented in context of IoT/ITS are needed to be considered. Among others, the resource-constrained mobile nodes are likely to move together with the vehicles or the human bodies in IoT/ITS. In this paper, a network-based mobility management scheme which supports group mobility to provide globally ubiquitous IoT/ITS services is proposed. As shown in the performance evaluation results, the proposed mobility management scheme can shorten handoff delay, reduce signaling overhead, and decrease power consumption in IoT/ITS.

Network Performance Aware Optimizations on IaaS Clouds

Network performance aware optimizations have long been a hot research topic to optimize distributed applications on traditional network environments. However, those optimization techniques rely on a few measurements on pair-wise network performance, and such direct use of network measurements is no longer valid on Infrastructure-as-a-service (IaaS) clouds. First, the direct calibration is ineffective. Network performance measurements may not represent the long-term performance (informally the stable component inside network performance) because of virtualization and network performance interference in the cloud. Second, the direct calibration is inefficient because the measurement overhead of all pair-wise link performance in a cluster becomes prohibitively high as the number of instances increases. To effectively and efficiently utilize existing network performance aware optimizations on IaaS clouds, we propose to reduce the measurement overhead and decouple the constant component from the dynamic network performance while minimizing the difference between the network performance and the constant component. For effectiveness, we use the constant component to guide the network performance aware optimizations. For efficiency, we exploit a non-negative matrix factorization (NMF) method to reduce the calibration overhead. Furthermore, we observe a tradeoff between effectiveness and efficiency, and develop an adaptive approach to capture this tradeoff. We demonstrate effectiveness and efficiency of our approach by adopting network performance aware optimizations on two kinds of basic applications, collective communications of MPI and generic topology mapping, and two real-world applications, namely N-body and conjugate gradient (CG). Our experiments on Amazon EC2 and simulations demonstrate significant calibration overhead reduction and performance improvement on guiding network performance aware optimizations, when comparing our approach to other state-of-the-art approaches.

UM Paging: Unified M2M Paging with Optimal DRX Cycle

In Machine-to-Machine (M2M) communications, devices can automatically communicate with each other without any intervention by humans. However, LTE-A is primarily designed and optimized for Human-to-Human (H2H) communications. Even though LTE-A has defined Machine-Type Communications (MTC), several parts are still undefined or follow the existing H2H standards. Specifically, the existing paging mechanism in LTE-A will substantially increase the computational load and signaling overhead of the system when there are many M2M devices. Therefore, we propose an effective solution to mitigate this problem. Compared with the traditional paging mechanism defined in LTE-A, the proposed solution can save more energy for M2M devices, reduce signaling overhead and alleviate the network's load significantly.

Artificial Noise-Aided User Scheduling for Optimal Secrecy Multiuser Diversity

In this letter, we propose a user scheduling scheme, which enables non-scheduled users to generate artificial noise in order to enhance wireless security in a multiuser wiretap network, where there exist $N$ users, a single desired receiver, and $K$ eavesdroppers. Our proposed scheme can achieve the optimal multiuser diversity gain (i.e., $\log \log N$ ) by adopting appropriate threshold values for scheduling feedback, which is also able to significantly reduce signaling overhead. We mathematically prove the achievability based on the derivation of a secure user-scaling law . Simulation results demonstrate that the proposed scheme achieves a better average secrecy rate than the conventional scheme in a finite $N$ case, even if both schemes obtain the same secure user-scaling law as $N \rightarrow \infty $ .

High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches

Massive multiple-input–multiple-output (MIMO) has been regarded as one of the key technologies for fifth-generation wireless networks, as it can significantly improve both the spectral efficiency and the energy efficiency. The availability of high-dimensional channel side information (CSI) is critical for its promised performance gains, but the overhead of acquiring CSI may potentially deplete the available radio resources. Fortunately, it has recently been discovered that harnessing various sparsity structures in massive MIMO channels can lead to significant overhead reduction, and thus improve the system performance. This paper presents and discusses the use of sparsity-inspired CSI acquisition techniques for massive MIMO, as well as the underlying mathematical theory. Sparsity-inspired approaches for both frequency-division duplexing and time-division duplexing massive MIMO systems will be examined and compared from an overall system perspective, including the design tradeoffs between the two duplexing modes, computational complexity of acquisition algorithms, and applicability of sparsity structures. Meanwhile, some future prospects for research on high-dimensional CSI acquisition to meet practical demands will be identified.

Dynamic clustering and management of mobile wireless sensor networks

In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDC-relay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to 53%.