Control Signaling Overhead Research Articles

Performance Analysis of Coordinated Interference Mitigation Approach for Automotive Radar

Millimeter automotive radar has great potential in Advanced Driver Assistance Systems (ADAS) to enable safety features such as adaptive cruise control and collision avoidance. However, with widely deployment of millimeter radars on vehicles, the risk of radar mutual interference becomes a major factor limiting the high performance of radar detection. In this paper, we analyze the mutual interference among multiple Frequency Modulated Continuous Wave (FMCW) radars. On one hand, we study the interference in detail by considering co-channel interference and adjacent channel interference simultaneously. Besides, the co-channel interference is analyzed by employing stochastic geometry model while the adjacent channel interference is assessed by the deterministic analysis method. On the other hand, we propose a Time-Frequency Division Multiple Access (TFDMA) scheme to mitigate the interference in a coordinated manner and evaluate it in terms of mitigation delay, the probability of interference, effective detectable density, maximum number of interference-free radar, and control signaling overhead. Finally, we study the power allocation strategy to enable the effectiveness of the coordinated interference mitigation approach based on the interference analysis. Simulation results verify the proposed framework for interference analysis by employing Monte Carlo method, and the performance improvement of the coordinated interference mitigation approach is 3.5 dB.

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

In the emerging high mobility vehicle-to-everything (V2X) communications using millimeter wave (mmWave) and sub-THz, multiple-input multiple-output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time (ST) domain (i.e., directions or arrival/departure and delays). Algebraic low-rank (LR) channel estimation exploits ST channel sparsity through the computation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">position-dependent</i> MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles’ geographical positions and tens to hundreds of training vehicles’ passages for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">each</i> position, leading to significant complexity and control signaling overhead. Here, we design a deep-learning (DL)-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single least squares (LS) channel estimate and without needing vehicle’s position information. Numerical results show that the proposed method attains comparable mean squared error (mse) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different ST channel features, providing comparable mse performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios.

Resource-Hopping-Based Grant-Free Multiple Access for 6G-Enabled Massive IoT Networks

Grant-free multiple access (GFMA) is an emerging technology to accommodate a massive number of devices for 6G-enabled Internet of Things (IoT) networks. The main advantages of GFMA are to efficiently reduce control signaling overhead for resource scheduling while improving resource efficiency. In this article, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">novel</i> resource-hopping-based GFMA (RH-GFMA) framework with resource hopping schemes for providing massive connectivity in 6G cellular IoT networks, where each IoT device is allowed to access physical radio resources by using a preassigned resource hopping pattern without not only resource request but also grant procedure, which is the so-called “one-shot” noninteractive multiple access. We exploit three types of resource hopping schemes in the proposed RH-GFMA framework: 1) random hopping; 2) resource group hopping; and 3) Latin-square group hopping. We mathematically analyze the RH-GFMA system performance in terms of the hopping pattern collision probability, maximum allowable packet delay, and interference-over-thermal. Finally, we derive an accommodation capacity of the proposed RH-GFMA framework, which is defined as the expected number of IoT devices accommodated in a cell under a maximum allowable packet-delay requirement and an interference-over-thermal constraint. With the proposed GFMA, massive IoT devices are expected to be efficiently accommodated in 6G wireless networks, while satisfying strict latency and reliability requirements.

A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC is aimed at providing connectivity to tens of billions of sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resources. Nonorthogonal multiple access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing‐based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article is aimed at providing an overview of the current state‐of‐art research work in various compressive sensing‐based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby providing useful insights for researchers to make further contributions in NOMA using compressive sensing techniques. Nonorthogonal CDMA massive connectivity grant free medium access compressive sensing multiuser detection

Edge Cloud-Enabled Radio Resource Management for Co-Operative Automated Driving

Co-operative automated driving (CAD) is a key fifth generation mobile networks (5G) use case in which autonomous vehicles communicate over vehicle-to-vehicle (V2V) links requiring a wide range of rate-reliability-delay performance. One key 5G enabler for CAD sidelink radio resource management (RRM) in a multi-operator environment is the virtualization of RRM at the cloud server. This, however, is challenging due to an increase in control plane delay, signaling overhead and complexity. This paper introduces an edge cloud-enabled end-to-end vehicle-to-everything (V2X) architecture to support sidelink RRM in CAD scenarios. Analyzing the problem of a cloud-based sidelink resource allocation for CAD, a utility-based multi-objective optimization problem is described and is translated to three tasks: 1) a vehicle cluster formation as a solution to the clique partitioning problem ensuring vehicle reachability on the control plane, 2) an inter-cluster resource block pool (RB-pool) allocation as a solution to a max-min fairness problem and 3) an intra-cluster resource allocation. The proposed solution framework aims to achieve high modularity, low complexity and decouples cluster formation and RB-pool assignment from the intra-cluster optimum resource allocation, which may be performed on different time scales at different edge cloud entities. Simulation results in a realistic vehicular deployment show significant gains in terms of sidelink throughput and delay while maintaining high link quality.

Design and experimental evaluation of OnDemand inter-domain mobility in SDN supported PMIPv6

The soaring number of mobile devices and their demands for enhanced services have forced operators to adopt locally software defined and globally distributed multi-domain systems. Network managed IP mobility across these domains is significant, and needs to be handled at service level granularity with OnDemand approach for resource efficiency and minimal transmission delay. Proxy Mobile IPv6 (PMIPv6) is a well-established network initiated local mobility solution. This paper evolves PMIPv6 to present a distributed architecture for OnDemand mobility across multiple domains with Software Defined Networking (SDN). In our proposed solution, controllers in SDN enabled PMIPv6 domains perform distributed communication with each other to provide inter-domain mobility. A novel home network prefix retrieval mechanism allows the domains to reclaim the prefixes once they are released by the mobile node. We also propose a route convergence based path update technique to reduce the transmission delay; and present a detailed experimental and analytical performance evaluation for handover delay, transmission delay, and control signaling overhead. The results show 4–9% and 30–45% gain over current work in handover delay and transmission delay, respectively.

Vehicle-Centric Network Association in Heterogeneous Vehicle-to-Vehicle Networks

Safe and reliable operation of future autonomous vehicles requires ultra-low latency vehicle-to-vehicle (V2V) networking. However, due to scalability issues of purely ad hoc network, vehicle-to-infrastructure (V2I) communication is still in demand. To avoid interference between V2V and V2I communications, conventional network association such as handover may generate a large amount of control and signaling overhead, especially in environments with high-speed mobility and small-coverage access points. Taking the additional latency caused by the overhead into consideration, a vehicle-centric approach is proposed to guarantee the low-latency requirements of the V2V networks. Different from solely optimizing the latency performance, the proposed approach focuses on effectively operating the network association and resources re-allocation. This avoids the massive exchange of control information and signaling and reduces its impact on latency. The proposed framework for V2V networking can underlay different virtual cells with a constrained network association rate and we can derive the corresponding upper bound of the latency performance. Analysis and simulation results indicate that our scheme successfully guarantees the upper bound of the latency and simultaneously constrains the network association rate, thus limiting the additional control exchange and signaling overhead.

A Low-Latency Transmission Method for Massive MIMO Systems With Low Control Signaling Overhead

In this paper, we consider the uplink of massive MIMO systems where the channel capacity is almost deterministic and varies slowly over time. We exploit this fact, by appropriately choosing the length of the codeword only once based on an initial estimate of the uplink channel capacity fed back by the base station (BS). The user terminal (UT) then transmits the codeword over consecutive coherence time intervals without waiting for any acknowledgement (ACK/NACK) from the BS. The BS sends only a single ACK/NACK after it completes decoding. Therefore, the proposed method has significantly less latency and signaling overhead when compared with stop-and-wait type hybrid automatic repeat request (HARQ) methods (as in LTE uplink) where a lot of delay is introduced as the UT needs to wait for the BS to signal the ACK/NACK for every retransmission. Analysis reveals that for sufficiently large number of antennas at the BS, the average codeword length of the proposed method (in number of coherence intervals) is the same as the average number of retransmissions (in number of coherence intervals) required by incremental redundancy (IR)-based HARQ and also that the energy efficiency of the proposed method and IR-HARQ is the same.

Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation for Grant-Free NOMA Systems

This paper concerns uplink grant-free nonorthogonal multiple access, where the handshaking procedure is not required to reduce control signaling overhead and transmission latency. In especially the dynamic scenarios, e.g., Internet of vehicles, the active users have to be identified and their channel state information needs to be estimated before performing multiuser detection. We investigate the joint user activity detection (UAD) and channel estimation (CE), which provides necessary information for data detection. In this paper, the joint UAD and CE is formulated as a block sparse signal recovery problem. First, the block orthogonal matching pursuit (BOMP) algorithm is studied for this problem, but its complexity grows with the fourth power of active user number, which hinders its application. Then, block sparse Bayesian learning (BSBL) is investigated to solve this problem, and in particular a low complexity message passing based implementation of BSBL with belief propagation and mean field is developed. The proposed message passing based BSBL (MP-BSBL) algorithm has a complexity independent of active user number, which can be significantly lower than that of the BOMP algorithm. In addition, MP-BSBL provides an estimate of the noise power, which can be readily used for data detection. Simulation results show that the MP-BSBL algorithm delivers almost the same performance as BOMP with the exact knowledge of active user number and can reach the performance bound for channel estimation.

A novel compressed sensing-based non-orthogonal multiple access scheme for massive MTC in 5G systems

The main challenges for massive machine type communication in 5G system are to support random access for massive users and to control signaling overhead and data processing complexity. To address these challenges, we propose a novel compressed sensing (CS)-based non-orthogonal multiple access (NOMA) scheme, called CS-NOMA, which introduces low coherence spreading (LCS) signatures to enable joint activity and data detection without requiring the activity information of users in advance. We present a sufficient condition for the construction of the LCS signatures to ensure that a CS-based multi-user detection (CS-MUD) can be effectively deployed in base station. Furthermore, we study the CS-NOMA scheme with imperfect channel state information (CSI) and present a bound for the performance of the CS-NOMA scheme. Simulation results show that the proposed scheme achieves a relatively high system overload (up to 4) when the active users are relatively sparse with an activity ratio of 1%, which implies that the CS-NOMA scheme can significantly improve the spectral efficiency, avoid the control signaling overhead, and reduce the transmission latency.

Compressive Sensing Multi-User Detection for MC-CDMA System in Machine to Machine Communication

Machine to Machine (M2M) Communication is one of the core technologies of 5G in the future. To relieve control signal overhead lead by massive access during M2M communication, we use greedy algorithm from the field of CS to perform joint Multi-User (MUD) detection of activity and data by taking advantage of sporadic nature of certain M2M applications. The simulation results show that it is effective in recovering sparse input signals as well as gaining higher spectral efficiency. We also show that combining cross validation (CV) with the greedy algorithm is reliable in identifying the sparsity level of input vector which makes CS-MUD more practical.

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.

Software Defined Wireless Networks: A Survey of Issues and Solutions

Wireless networks such as mobile networks, with their inflexible and expensive network infrastructure, are facing various challenges in efficiently handling the exponentially growing traffic demands of users. Hence, mobile network operators (MNOs) are looking forward to novel networking paradigms which could simplify the task of network management and control and allow faster deployment of newer solutions on top of existing hardware by software updates. Software defined networking (SDN) is a revolutionary technology which makes networks more agile and flexible by separation of data plane and control plane tasks. SDN is playing a key role in deploying mobile network services on network function virtualization (NFV) infrastructure for elastic and flexible deployment of core network services e.g., virtualization of evolved packet core in LTE to efficiently handle huge control signal overhead and traffic demands from machine-to-machine and internet of things devices. Besides, NFV and SDN are offering scalable, flexible and adaptable network service chaining platforms as a replacement for inflexible middleboxes. Mobile edge computing (MEC) is a new evolving platform which brings together IT services and cloud computing for offering end-users network aware services and solutions. In this survey, we enumerate numerous issues and challenges in designing SDN based wireless networks and review various SDN based seminal solutions for 4G/5G. Finally, this survey presents the role of SDN, NFV and MEC in designing 5G networks.

Reduction of control signal overhead for electric vehicle charging operation in smart grid system

Smart grid systems have been proposed to replace the conventional power distribution system in order to accommodate future market penetration of electric vehicles (EVs). Nevertheless, there are many factors to consider when large numbers of EVs require simultaneous charging in the smart grid. Among various optimization schemes, the most updated schemes proposed charging with coordination to optimize EV charging performance. In this case, huge amount of control signals are involved in the coordinated charging, therefore the charging performance is retarded. In this paper, a new threshold-based charging operation with historical average data of the EV charging system to improve the EV charging performance is proposed by minimizing control signal overhead, instead of maximum power delivery to all EVs. The moving average of historical data for BSOC levels of the EVs can properly approximate the charging profile of the EVs over time thus enabling the development of an optimization algorithm based on the threshold method. Up to 33% of the reduction in the control signal overhead is achieved with slight trade off in the power delivery to the EVs.

Impact of Microscopic Vehicle Mobility on Cluster-Based Routing Overhead in &lt;roman&gt;VANETs&lt;/roman&gt;

Node clustering is a potential solution to minimize the control signaling overhead of routing protocols in vehicular ad hoc networks (VANETs). High relative vehicle mobility and frequent network topology changes induce instability to node clusters. Node cluster instability inflicts new challenges in maintaining a long route between network nodes, thus increasing the routing overhead. As a result, cluster instability, which is foisted by vehicle mobility, is a crucial issue for cluster-based routing in VANETs. This paper presents a stochastic analysis of the impact of cluster instability on generic routing overhead. A stochastic cluster instability model is adopted to capture the time variations of the cluster structure in terms of the cluster membership change rate and the cluster-overlap state change rate. First, we derive the probability distribution of the intracluster routing overhead using the cluster membership change rate. Second, the intercluster routing overhead is modeled as a rooted tree, with the tree nodes representing the value of the overhead and the tree edges weighted by the probability of a cluster-overlap state change. Numerical results are presented to evaluate the proposed models, which demonstrate a close agreement between analytical and simulation results.

Throughput in A Cooperative Network and Channel State Information

Cooperative communications greatly enhance the point-to-point link capability to against channel fading, and such performance gain is expected to large wireless ad hoc networks. However, current cooperative networking is based on ideal assumptions of completely known network information and centralized optimization, which is practically infeasible to large ad hoc networks requiring unscalable control signaling overhead. In this paper, the exact throughput of practical cooperative ad hoc network is provided, in which users make autonomous decisions with regard to their network usage based on the current network conditions and their individual preferences. Since preference of each node is shown by the achievable data rate estimated from the channel state information (CSI) of links of each source-destination pair, the cost of acquiring CSI is considered in the throughput analysis. Furthermore, the cooperation beneficial condition and the operation algorithm of each node to guarantee the network operating at the highest throughput are proposed. The proposed algorithm provides a way to control network-level performance by local operations among nodes.

Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype

Software defined wireless networks (SDWNs) present an innovative framework for virtualized network control and flexible architecture design of wireless sensor networks (WSNs). However, the decoupled control and data planes and the logically centralized control in SDWNs may cause high energy consumption and resource waste during system operation, hindering their application in WSNs. In this paper, we propose a software defined WSN (SDWSN) prototype to improve the energy efficiency and adaptability of WSNs for environmental monitoring applications, taking into account the constraints of WSNs in terms of energy, radio resources, and computational capabilities, and the value redundancy and distributed nature of data flows in periodic transmissions for monitoring applications. Particularly, we design a reinforcement learning based mechanism to perform value-redundancy filtering and load-balancing routing according to the values and distribution of data flows, respectively, in order to improve the energy efficiency and self-adaptability to environmental changes for WSNs. The optimal matching rules in flow table are designed to curb the control signaling overhead and balance the distribution of data flows for achieving in-network fusion in data plane with guaranteed quality of service (QoS). Experiment results show that the proposed SDWSN prototype can effectively improve the energy efficiency and self-adaptability of environmental monitoring WSNs with QoS.

Impact of Feedback Channel Delay over Joint User Scheduling Scheme and Separated Random User Scheduling Scheme in LTE-A System with Carrier Aggregation

In Long Term Evolution-Advanced (LTE-A), the signal quality in a wireless channel is estimated based on the channel quality measurements. The measurement results are used to select suitable modulation and coding scheme for each transmission, feedback, and processing delay, which can cause a mismatch between channel quality information (CQI) and current channel state at the base station. However, prospect delays in the reception of such CQI may lead to a system performance degradation. This study analyzes the impact of CQI feedback delay on joint user scheduling (JUS) scheme and separated random user scheduling (SRUS) scheme in LTE-A system over carrier aggregation. The analysis will be compared with the system having delayed channel and perfect knowledge at different deployment scenario. We will study the throughput performance of both scheduling schemes with different deployment scenario, and then recommend the suitable deployment scenario to keep the desired QoS for a specific number of users. Results show that, in main beam directed at sector boundaries and diverse coverage, JUS scheme performs better than SRUS, which can justify the intensive use of user equipment power and extra control signaling overhead.

On functionality separation for green mobile networks: concept study over LTE

Traditional wireless networks are designed for ubiquitous network access provision with low-rate voice services, which thus preserve the homogeneous architecture and tight coupling for infrastructures such as base stations. With the traffic explosion and the paradigm shift from voice-oriented services to data-oriented services, traditional homogeneous architecture no longer maintains its optimality, and heterogeneous deployment with flexible network control capability becomes a promising evolution direction. To achieve this goal, in this article, we propose a two-layer network functionality separation scheme, targeting at low control signaling overhead and flexible network reconfiguration for future mobile networks. The proposed scheme is shown to support all kinds of user activities defined in current networks. Moreover, we give two examples to illustrate how the proposed scheme can be applied to multicarrier networks and suggest two important design principles for future green networks. Numerical results show that the proposed scheme achieves significant energy reduction over traditional LTE networks, and can be recommended as a candidate solution for future green mobile networks.

A novel multicast scheme for feedback-based multicast services over wireless networks

Recently, a point-to-multipoint transmission is a promising technology in the limitation of wireless resources and surging of subscribers, and an optimal to deliver real-time streaming service such as video/audio conference, digital live cast, and so on. There are two types of point-to-multipoint transmission; broadcast and multicast. Broadcast services deliver the information to all receivers in a cell and multicast services deliver the information to a multicasting group. Traditional approaches to multicast services operate by radiating transmission power with the lowest modulation level and coding rate which may be much lower than the lowest acceptable rate for all the multicast subscribers of a multicasting group. While such an approach has the advantage of simple implementation, this method may incur substantial performance penalties such as low spectral efficiency and a large amount of power consumption. To solve this issue, wireless multicasting with channel state information has been investigated in many research studies. However, the overhead of feedback information is a heavy drawback to the achievement of good network performance. In feedback-based multicast services, the system performance is also mostly dependent on the receivers with the lowest channel quality in a multicasting group. Hence, receivers who have better channel condition can receive multicast data successfully without channel quality indicator (CQI) feedback. In this point of view, the feedback-based multicast scheme should aim to achieve high resource efficiency by reducing unnecessary CQI feedback and transmission optimization by dynamically adapting modulation and coding selection (MCS) level according to the variations of the channel state. We show that the proposed scheme enhances the system throughput by dynamically adapting the MCS level according to variations of the CQI and also reduce CQI feedback by transiting from a feedback state to a non-feedback state according to a channel gain and recent frame error rate of each receiver. Finally, the proposed scheme does not need any additional control signaling overhead and also can be expanded and employed in any kind of systems, such as WCDMA, WiMAX, LTE, and LTE-Advanced.