QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

Advanced metering infrastructure targets the real-time collection of massive multisource data with smart meters (SMs), forming a key component of active distribution networks (ADNs). To achieve comprehensive situation awareness for ADNs, the use of high-speed and reliable two-way communications, such as 5G communications, is the basis. However, it is challenging to optimally allocate wireless resources for massive data, especially when emergency events occur randomly. This study discusses an energy-efficient and optimal wireless resource allocation method employing 5G uplink-based machine-type communications for achieving ADN situation awareness. We propose a 5G-based framework that can reasonably reserve resource blocks for emergency data and model the resource allocation scheme as a problem of maximizing energy efficiency (EE). Then, by using the sample average approximation theory, we transform the stochastic constraint and summarize the scheme as a mixed-integer linear programming problem. Finally, we construct an iterative algorithm based on the Lagrange dual algorithm for searching the global optimal allocation scheme. The results of simulations and experiments illustrate that even if emergency events occur randomly, our algorithm can optimally support the cooperative transmission of all sampled data while providing the best EE for large-scale SM communications.

For a multiuser cognitive OFDM network, most existing adaptive resource allocation methods focus solely on fixed data-rate requirement (QoS guaranteed) or variable data-rate (no QoS guaranteed) conditions without any fairness consideration. In this paper, we investigate the resource allocation problem in a heterogeneous cognitive network with different QoS requirements. By decomposing the problem as a convex optimization, an optimal time-sharing resource allocation method is proposed to maximize the throughput of cognitive network under both subcarrier interference temperature limits and heterogeneous data-rate requirements. Finally, the performance of our proposed resource allocation algorithm is investigated by numerical results.

Currently, many sensor data networks are managed using machine-to-machine (M2M) or machine type communications (MTCs) technologies. One of the key factors for the success of sensor data networks is in developments of international M2M/MTC standards. As third generation partnership project (3GPP) and oneM2M are actively working on standardising M2M/MTC specifications for 3GPP core networks and M2M common service layer, this study reviews the ongoing activities in 3GPP and oneM2M standardisation bodies, in the area of machine type or M2M communications, focusing on enhancing device, service platform, and mobile network technologies that can be useful in achieving cost efficiencies in sensor data networks.

As one of the most important communication scenarios in the coming fifth generation (5G) cellular networks, ultrareliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems. However, one of the biggest challenges is that how to integrate URLLC and control performance together to maximize the overall system performance. In this paper, we investigate the resource allocation for URLLC uplink in real-time wireless control systems. Specifically, we first discuss the relationship between communication and control performance. Based on that, we convert the hybrid co-design problem into a regular wireless resource allocation problem. Then, we propose an iteration algorithm to obtain the optimal wireless resource allocation. Simulation results indicate the performance of our method.

Machine Type Communication (MTC) is machine to machine communications in cellular network. It plays the key role for intelligent services and there is a great market prospect for these services. Comparing with H2H communications, there are significant changes in service features. In 3GPP Release-13, there are three objectives of enhancement for MTC, including UE complexity reduction, coverage enhancement and UE power consumption reduction. The supported RF bandwidth has been agreed as 1.4MHZ and repetition technique for channels is considered to achieve coverage improvement. However, due to these improvement, if the conventional method continues to be used, the collision probability of PRACH transmission and the power consumption of MTC UEs will be higher than the legacy UEs. In order to alleviate this issue, this paper concentrates on the random access procedure on low cost MTC and it takes the coverage enhancement level into the consideration which isn't the dimension of legacy LTE system. It proposes a method about new mapping relationship between PRACH resource set and coverage enhancement level. By this way, it can reduce the collision probability and improve the utilization of radio resources, which consequently increases the success probability of random access and reduces the power consumption. Finally, the simulation result shows that the method can improve the performance of low cost MTC.

With a wide range of potential applications, machine-type communication (MTC) or machine to machine (M2M) communications is gaining a tremendous interest among mobile network operators and research groups. MTC is standardized by the 3rd Generation Partnership Project (3GPP), which has been regarded as the promising solution facilitating M2M communications. One of research challenges in MTC is security, if the security is not well addressed, all applications involving MTC cannot be put into market. In the latest standard, the 3GPP proposes a novel scenario for MTC, where MTC server is located outside of the operator domain. However, the connection between 3GPP core network and MTC server in this scenario is insecure, as a result, there are distrustful relationships among MTC device, core network and MTC server. To resolve this problem, we propose an end-to-end security scheme for MTC based on the proxy-signature technique in this paper. Specifically, both the MTC device and the MTC server can establish a strong trustful relationship with each other through using the proxy signatures issued by the core network. Through security analysis, we conclude that the proposed scheme can achieve the security goals, and prevent the various security threats. In addition, performance evaluation also demonstrates its efficiency in terms of operational cost during authentication.

The next generation cellular networks are expected to provide massive connectivity with support for high data-rate applications. Machine type communication (MTC) is one of the emerging technologies envisaged to facilitate large-scale provisioning of diverse services with varying quality of service (QoS) requirements. Spectrum aggregation (SA) is one of the promising techniques to increase capacity for high data rate applications and services. In this paper, cognitive radio (CR) MTC (CR-MTC) framework with spectrum aggregation (SA) in cellular networks is considered. The CR-MTC comprises MTC gateway (MTCG) through which data from MTC devices (MTCDs) is relayed to the base-station (BS). In order to support high data-rates, the MTCG employs SA for concurrent transmissions through multiple channels. Performance of SA over fading wireless channels is therefore presented, based on the developed analytical models for ergodic capacity and outage probability. The presented simulation and analytical results reveal that performance of SA in wireless channels is subject to fading, wherefore it can be deduced that SA is mainly beneficial in high signal-to-noise (SNR) ratio regime.

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 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.

Machine Type Communication (MTC) is becoming an integral part of the Long Term Evolution — Advanced (LTE-A) cellular network. Challenges arise when some of the MTC devices, due to the nature of their applications, are deployed in low signal locations. As per 3GPP requirements, there is a need for additional coverage enhancement up to 20 dB in comparison with LTE category 1 UE for MTC devices. In the previous works reported till now, Repetition Coding is proposed as an effective technique to achieve the required coverage enhancements at cost of longer decoding time. In low signal conditions where many repetitions are required to build the SNR needed, the decoding delay may be unacceptable. For a LTE-A MTC UE, Physical Broadcast CHannel (PBCH) decoding has a very important role and fast, efficient decoding of PBCH will help to improve the device performance. In this paper, we propose to use well established technique called Reduced Search (RS) Viterbi to improve PBCH decoding performance without compromising the time-to-decode. RS Viterbi technique utilizes a priori knowledge of transmitted bits to reduce the size and complexity of trellis, which in turn also reduces probability of choosing incorrect path, i.e., error. Up to 2.2 dB SNR gain is seen in simulation using the RS Viterbi decoding against the conventional Viterbi decoding, which will contribute in improving the sensitivity of MTC devices for better reachability.

Machine Type Communications (MTC) collectively refers to the exchange of data among devices that operate without human intervention. A significant number of such devices are currently served by cellular networks, and that number is expected to grow in the near future. However, cellular networks, including current fourth generation LTE networks, face several challenges when it comes to handling MTC traffic as they were primarily designed for Human Type Communications (HTC) which have very different traffic patterns. In this paper we focus on periodic MTC devices, such as sensors and meters, which cause significant signaling load and increased collisions over standard LTE networks. We propose ASPIS, a holistic mechanism designed to overcome these problems. ASPIS reduces the signaling load by partly preserving a device's connection to the network in conjunction with a new Random Access process and efficient support for short message transmissions. In addition, it uses a proactive preamble split scheme to alleviate collisions. ASPIS is easy to implement without requiring hardware changes while at the same time maintains security and can be incrementally deployed alongside legacy devices/infrastructure. We showcase the practicality of ASPIS by implementing it on the OpenAirInterface platform. Further, we demonstrate its effectiveness through extensive evaluations via a combination of small-scale experimental evaluation and large-scale, realistic simulations.

Future cellular networks are expected to support new communication paradigms such as machine-type communication (MTC) services along with human-type communication (HTC) services. This requires base stations to serve a large number of devices in relatively short channel coherence intervals which renders allocation of orthogonal pilot sequence per-device approaches impractical. Furthermore, the stringent power constraints, place-and-play type connectivity and various data rate requirements of MTC devices make it impossible for the traditional cellular architecture to accommodate MTC and HTC services together. Massive multiple-input-multiple-output (MaMIMO) technology has the potential to allow the coexistence of HTC and MTC services, thanks to its inherent spatial multiplexing properties and low transmission power requirements. In this work, we investigate the performance of a single cell under a shared physical channel assumption for MTC and HTC services and propose a novel scheme for sharing the time-frequency resources. The analysis reveals that MaMIMO can significantly enhance the performance of such a setup and allow the inclusion of MTC services into the cellular networks without requiring additional resources.

Machine type communication (MTC), as one of the most promising technologies in the future wireless communication, has brought mobile communication network into a new level. The breakthrough of cutting-edge technology and broad coverage of cellular networks in long term evolution advanced network constitute an ideal platform for ubiquitous MTC service provisioning on a large scale. However, under the traditional attach approach, the massive MTC devices always select the evolved NodeB (eNB) with the best signal quality for the attachment, thereby causing the network congestion and overload. As a result, it is necessary to design an efficient eNB selection scheme to avoid overload issue. In this paper, by modeling MTC arrivals using nonhomogeneous poisson process with the memoryless property, we formulate the eNB selection problem as a Markov decision process (MDP) and try to compute an optimal solution. Since the network parameters for MDP are not obtained easily, a learning-based algorithm with value-difference based exploration (VDBE) policy who could leverage both present conditions and expected future demands is further proposed. The performances of the proposed reinforcement learning (RL) VDBE, other RL-based, the legacy best-signal-quality, and MDP without RL eNB-selection schemes are analyzed in terms of blocking probability, transmission rate, and load balancing. The simulation results show that our scheme has the best performance on the blocking probability and loading balancing while an acceptable result on the transmission rate. It is suitable for the MTC environment with a highly changed number of MTC arrivals while without high requirement on the transmission rate.

During emergencies LTE/5G-based public mobile land networks (PLMNs) restrict network access by normal users, which means a lack of service reliability which limits application of LTE/5G for machine-type communication (MTC) in critical applications, such as power systems This paper shows how existing LTE/5G features can be used to differentiate MTC of devices in a microgrid from other MTC or human-to-human (H2H) communication and ensure that these microgrid devices have service during emergencies, which enables use of LTE/5G communication to co-ordinate the use of distributed energy resources (DER) in microgrids, so that they can autarkically perform blackout recovery of an islanded microgrid. It is shown that this method allows the blackout recovery 100 times faster than with a conventional black start. The microgrid blackout recovery is demonstrated using the LTE/5G PLMN Access Barring feature. The disadvantage of using PLMN-based Access Barring is the need to define two separate PLMNs in one radio cell, which is an inefficient use of radio spectrum. However, this can be avoided by using the Extended Access Class Barring (EAB) override or application-specific congestion control (ACDC) features of the CAT-M1 low-power wide-area MTC technology, which are included in LTE and 5G standards.