6G Schedule and Application Traffic Alignment for Efficient Radio Resource Utilization

In order to reduce handover delay caused by handover failure, radio resource is reserved for handover users not only at target cell, but also at prepared cells. This has been agreed in 3GPP LTE. However, these reserved resources cannot be assigned for other users until the finishing of the handover, which results in inefficiency of radio resource utilization. This paper presents a new resource reservation scheme for handover users to achieve fast handover failure recovery and efficient radio resource utilization. By proposed scheme, a resource pool at prepared cells is shared among multiple users, and the number of prepared cells is dynamically set according to the service type of users. Numerical analysis and simulation results show that the proposed scheme can improve successful handover probability and reduce handover delay.

The evolution of cellular networks from one generation to another has led to the deployment of multiple radio access technologies (such as 2G/2.5G/3G/4G) in the same geographical area. This scenario is termed heterogeneous cellular networks. In heterogeneous cellular networks, radio resources can be jointly or independently managed. When radio resources are jointly managed, joint call admission control algorithms are needed for making radio access technology selection decisions. This chapter gives an overview of joint call admission control in heterogeneous cellular networks. It then presents a model of load-based joint call admission control algorithm. Four different scenarios of call admission control in heterogeneous cellular networks are analyzed and compared. Simulations results are given to show the effectiveness of call admission control in the different scenarios. The coexistence of different cellular networks in the same geographical area necessitates joint radio resource management (JRRM) for enhanced QoS provisioning and efficient radio resource utilization. The concept of JRRM arises in order to efficiently manage the common pool of radio resources that are available in each of the existing radio access technologies (RATs) (Perez-Romero et al, 2005). In heterogeneous cellular networks, the radio resource pool consists of resources that are available in a set of cells, typically under the control of a radio network controller or a base station controller. There are a number of motivations for heterogeneous wireless networks. These motivations are (1) limitation of a single radio access technology (RAT), (2) users’ demand for advanced services and complementary features of different RATs, and (3) evolution of wireless technology. Every RAT is limited in one or more of the following: data rate, coverage, security-level, type of services, and quality of service it can provide, etc. (Vidales et al, 2005). A motivation for heterogeneous cellular networks arises from the fact that no single RAT can provide ubiquitous coverage and continuous high QoS levels across multiple smart spaces, e.g. home, office, public smart spaces, etc. Moreover, increasing users’ demand for advanced services that consume a lot of network resources has made network researchers developed more and more spectrally efficient multiple access and modulation schemes to support these services. Consequently, wireless networks have evolved from one generation to another. However, due to huge investment in existing RATs, operators do not readily discard their existing RATs when they acquire new ones. This situation has led to coexistence of multiple RATs in the same geographical area.

The increasing use of wireless communications in mobile devices starts a new level of resource management. Users with mobile devices accessing wireless hot spots are a commonplace, and, thus, their management is becoming more important. Therefore, efficient utilization of radio resource becomes a key point in the design of broadband systems. From the view of the development of radio resource management, current technologies have quite good performance and high efficiency when the category of service is single. However, when multiple services take place simultaneously, the system has to emphasize on some certain kinds of services and degrade the performance of others. Moreover, the wireless network is not able to detect collision and monitor the channel status as is the wire line network, which makes it more difficult to design the management scheme. This paper introduces and analyzes current main technologies of radio resource management, discusses the key technical points on designing the scheme, and then offers a new proposal of uplink radio resource allocation scheme in broadband systems. Based on the detailed illustration of the algorithm, a simulation system is established to verify it, which is adapted to the typical situation of broadband wireless communication systems. The theory analysis and the simulation results show that the new method successfully resolves the uplink bandwidth allocation problem for the simultaneous multiple services, satisfies the different requirements of delay and bandwidth of different services, and also reduces the protocol wasting and transmission delay.

Driven by the exponential surge on high data rate services, network operators are facing the challenges of how to enhance the capacity and optimize the coverage in a cost-efficient approach. However, traditional network optimization technologies passively adjust the network configurations based on network's congestion ratio, drop-off rate, coverage holes, etc., leading to suboptimum user experiences. Therefore, the objective of this paper is to optimize the network configurations by obtaining the accurate network status, user demand, and application request distribution based on the real-time data. The data mining technique is introduced to predict the resource margin based on historical measurement statistics. To explore the dynamic distribution of user demand and application request, a weighted k-nearest neighbors model is proposed to predict periodic characteristics of network traffics, denoting different temporal and spatial patterns of radio resource margins. In contrast to the traditional passive network optimization approaches, the radio resources can be reconfigured actively to meet the dynamic patterns of traffic loads by using the proposed optimization algorithm. Results prove that the proposed data mining model can capture the dynamics of traffic loads to optimize the traffic load balance and increase the efficiency of radio resource utilization using the network statistic data.

The coexistence of different cellular networks in the same area necessitates joint radio resource management for enhanced QoS provisioning and efficient radio resource utilization. We propose adaptive bandwidth management and joint call admission control (JCAC) scheme for heterogeneous cellular networks. The objectives of the proposed adaptive JCAC scheme are to enhance average system utilization, guarantee QoS requirements of all accepted calls, and reduce new call blocking probability and handoff call dropping probability in heterogeneous wireless networks. We develop a Markov chain model for the adaptive JCAC scheme and derive new call blocking probability, handoff call dropping probability, and average system utilization. Performance of the proposed adaptive JCAC scheme is compared with that of nonadaptive JCAC scheme in the same heterogeneous wireless network. Results show an improvement in average system utilization of up to 20%. Results also show that connection-level QoS can be significantly improved by using the proposed adaptive JCAC scheme.

Mobility-aware clustering algorithms with interference constraints in wireless mesh networks

This paper discusses load balancing architecture among heterogeneous wireless networks (HWNs). Load balancing is an important issue for efficient use of radio resources not only in a single network but among different radio access technologies (RATs) in HWNs. Cooperative management of radio resources among heterogeneous RATs is considered to reduce imbalance of utilization of radio resources and enhance availability of the system. For effective load balancing, we define a ldquocommunityrdquo concept, in which radio resources of various RATs are managed together. Radio resource management functional entities of RATs cooperatively work in the community. We present a load balancing architecture applied to 3GPP LTE, WiBro and WLAN and describe architectural considerations. We also present a straightforward load balancing algorithm that takes account of bandwidth usage and received signal to noise ratio. The load balancing algorithm of community radio resource management enhances utilization of radio resources and reduces call blocking probability of individual RATs in the community and further enhances average performance of the whole community.

In this paper, we consider the saturation problem in the 3GPP LTE cellular system caused by the expected huge number of machine‐type communication (MTC) devices, leading to a significant impact on both machine‐to‐machine (M2M) and human‐to‐machine H2H traffic. M2M communications are expected to dominate traffic in LTE and beyond cellular networks. In order to address this problem, we proposed an advanced architecture designed for 5G LTE networks to enable the coexistence of H2H/M2M traffic, supported by different priority strategies to meet QoS for each traffic. The queuing strategy is implemented with an M2M gateway that manages four queues allocated to different types of MTC traffic. The optimal radio resource allocation method in LTE and beyond cellular networks was developed. This method is based on adaptive selection of channel bandwidth depending on the QoS requirements and priority traffic aggregation in the M2M gateway. Additionally, a new simulation model is proposed which can help in studying and analyzing the mutual impact between M2M and H2H traffic coexistence in 5G networks while considering high and low priority traffics for both M2M and H2H devices. This simulator automates the proposed method of optimal radio resource allocation between the M2M and H2H traffic to ensure the required QoS. Our simulation results proved that the proposed method improved the efficiency of radio resource utilization to 13% by optimizing the LTE frame formation process.

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.

An efficient resource allocation is one of the greatest challenges in wireless cellular communication. The resource allocation schemes avoid wastage of resources by allocating resources to a mobile terminal over a short period of time, providing quality of service over wireless networks is the most stressing point for service providers. In general a high degree of sharing is efficient, but requires service protection mechanisms to guarantee the Q o S for all services. In this paper we address the multi cell interference on overall radio resource utilization and propose a new strategy for resource allocation in multi cell systems. We also propose a joint management of interference within and between cells for allocation of radio resources, Simulation results are showing that there is a significant improvement in the resource utilization so that overall network performance.KeywordsMobile TerminalRadio ResourceResource Allocation SchemeInter Cell InterferenceEfficient Resource AllocationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Adaptive resource allocation with traffic peak duration prediction and admission control for cognitive Wi-Fi networks

Despite the popularity of mobile applications, their performance and energy bottlenecks remain hidden due to a lack of visibility into the resource-constrained mobile execution environment with potentially complex interaction with the application behavior. We design and implement ARO [1], mobile Application Resource Optimizer, the first tool that efficiently and accurately exposes the cross-layer interaction to enable the discovery of inefficient resource usage. System Overview. ARO consists of two components: the data collector and the analyzers. The data collector runs efficiently on a handset to capture information essential for understanding resource usage, user activity, and application performance. The collected traces are subsequently fed into the analyzers, which run on a PC, for offline analysis. We describe the workflow of ARO as outlined in Figure 1. 1. The ARO user invokes on her handset the data collector, which collects relevant data, i.e., all packets in both directions and user input. ARO also identifies the packet-to-application correspondence. This is used to distinguish the target application, i.e., the application to be profiled, from other applications simultaneously accessing the network. 2. The ARO user launches the target application and uses it as an end user. To obtain a representative understanding of the application studied, ARO can be used across multiple runs or by multiple users to obtain a comprehensive exploration of different usage scenarios of the target application. 3. The ARO user loads the ARO analysis component with the collected traces. ARO then configures the RRC analyzer with handset and carrier specific parameters, which influence the model used for analysis. The TCP, HTTP, and burst analyzers are generally applicable. 4. ARO then performs a series of analyses across several layers. In particular, the RRC (Radio Resource Control) state machine analysis accurately infers the radio states from packet traces so that ARO has a complete view of radio resource and radio energy utilization during the entire data collection period. ARO also performs transport protocol and application protocol analysis to associate each packet with its transport-layer functionality and its application-layer semantics. ARO next performs burst analysis, which utilizes aforementioned cross-layer analysis results, to understand the triggering factor of each short traffic burst, which is the key reason of low efficiency of resource utilization.

Joint utilization of orthogonal radio resources from multiple domains such as spatial, time-frequency, and delay-doppler domains has become an important paradigm to support diverse QoS requirements (higher datarate, higher spectral efficiency, and low latency) in beyond 5G, and 6G. However, due to higher carrier frequency (mmWave) communication with closely packed massive MIMO antennas, and high-speed mobility in future wireless channels, severe non-orthogonal interferences are dynamically induced in multiple domains which dramatically deteriorate the communication datarate of current OFDM systems. In high speed mobility scenarios, orthogonal time frequency space (OTFS) modulation scheme achieves better communication performance than OFDM at higher modulation cost. Based on these observations, this paper is motivated to propose a novel, situation-aware, cost efficient, switched modulation in spatial, time-frequency, and delay-doppler domains termed hybrid multi-dimensional modulation (H-MDM) scheme that jointly optimizes the radio resource separation to minimize the non-orthogonality degree in each domain, and thus achieves maximized communication datarate under dynamically varying non-orthogonality conditions in those domains. Simulation results validate that the proposed H-MDM achieves maximized datarate compared to state-of-art MIMO-OFDM, and MIMO-OTFS systems under such randomly varying non-orthogonality conditions. Furthermore, we demonstrate that the proposed H-MDM scheme is highly advantageous for high speed mobility, and massive MIMO communication.

Radio resource management (RRM) plays an extremely important role in efficient utilization of the limited radio resources to provide guaranteed quality of service (QoS). Power and transmission rate cotrol are considered as two primary RRM components in mobile cellular systems. The former method is well-known for upholding required signal quality and reducing the energy consumption, while the latter is used to maximize the system throughput. In this thesis, the algorithm of simultaneously adapting transmission power and data rate to maximize system throughput and minimize the power consumption for W-CDMA system is proposed [1]. The greedy rate packing (GRP) allocation scheme is applied in the rate adaptation, where the higher transmission rate is assigned to the users with better channel conditions meanwhile mminimizing the transmission power. It can be interpreted that more resources, such as power and transmission rate, are allocated at time instants when channel conditons are favourable. As a result, the resource allocation will be working in an efficient way. Another problem in power control is that using fixed stepsize power control (FSPC) can not fully react to the changing of the fading fluctuations when mobile speed changes. A novel dynamic stepsize power control (DSPC) algorithm [2] is presented as well, so as to enhance the system performance. The performance of the proposal algorithms are evaluated through computer simulations, and compared with the traditional approaches from the literature. We model and stmulate all major components of the system within an accurate frame/time slot structure specified in the UMTS W-CDMA system. Numerical results verify that significant performance improvement can be achieved with the proposal algorithms.

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies.