A survey of handover management in LTE-based multi-tier femtocell networks

The latest evolution of cellular technologies, i.e., 5G including long term evolution-advanced (LTE-A) Pro and 5G new radio promises enhancement to mobile technologies for the Internet of Things (IoT). Despite 5G’s vision to cater to IoT, yet some of the aspects are still optimized for human-to-human (H2H) communication. More specifically, the existing group paging mechanism in LTE-A Pro has not yet clearly defined approaches to group, mobile IoT devices (MIDs) having diverse characteristics, such as discontinuous reception (DRX) and data transmission frequency (DTF) with various mobility patterns. Inappropriate grouping of MIDs may lead to increased energy consumption and degraded quality of service, especially in terms of packet arrival delay (PAD) and packet loss rate (PLR). Therefore, in this paper, we devise novel models to estimate PAD, PLR, and energy consumption for MIDs, specifically for the group paging mechanism. Based on the proposed models, we formulate an optimization problem with the objective to minimize energy consumption of MIDs, while providing required PAD and PLR. The nonlinear convex optimization problem addressed herein is solved using the Lagrangian approach, and the Karush–Kuhn–Tucker conditions have been applied to derive optimal characteristics for MIDs to join the group, namely, DRX and DTF. The extensive numerical results presented verify the effectiveness of the proposed method, and the mathematical models demonstrate the superiority of our proposed approach over random grouping approach concerning significant energy consumption of MIDs.

Energy-Efficient Group Paging Mechanism for QoS Constrained Mobile IoT Devices Over LTE-A Pro Networks Under 5G

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.

eNB Selection for Machine Type Communications Using Reinforcement Learning Based Markov Decision Process

The Heterogeneous Network is widely used in the Fifth Generation wireless network to solve the problem of increasing demand on wireless communication. Femtocell or called Home-evolved NodeB (HeNB) is one of the small cells nominated to be used in this generation. HeNB is a low-power, low-cost, and short coverage area base station randomly assigned by the user. Therefore, HeNB is used in Long Term Evolution (LTE/LTE-Advanced) to support Quality of Service next to conventional cell. Based on the short range and dense use of HeNB, the seamless handover procedure is one of the challenges HeNet system is facing. Though there are numerous present literature work for handover decision issues, this paper attempts to present handover management challenges and the outline of modern handover decision algorithms in between the evolved NodeB (eNB) and HeNB. Furthermore, comprehensive details of the handover procedure in LTE-A Heterogeneous Networks are presented. This survey categorizes the recent studies in decision algorithms for the two-tier network (eNB and HeNB) regarding the main decision technique. Finally, a comprehensive summary of input parameters, techniques, and performance evaluation for each handover decision scheme are discussed by providing the advantage and disadvantage of each category.

A survey on femtocell handover management in dense heterogeneous 5G networks

Narrowband Internet of Things (NB-IoT) is a novel narrowband radio technology particularly designed for low power, low data rate, enhanced coverage, and long battery life. To save power, the discontinuous reception (DRX) mechanism is standardized by third generation partnership project and also is adopted in the NB-IoT specifications as important methods to obtain long battery life. However, current DRX schemes used in the Long Term Evolution (LTE) are inadequate to the large-scale NB-IoT system. In this paper, aim to obtain better power saving and balance weak-up latency performance as well as avoid the possible signaling storm in an NB-IoT system with massive devices, a grouping-based energy efficient DRX strategy is proposed. Moreover, different DRX schemes are devised for the group leader and group members to further reduce power consumption. A discrete time semi-Markov process is applied to numerically analyze the system performance and to obtain the power saving factor and weak-up latency. Simulations show the fact that how the various DRX parameters impact the system performance. Specifically, simulation results demonstrate our proposed DRX mechanism outperforms the LTE DRX scheme in terms of power-saving performance although at the slight and sustainable cost of latency.

Grouping-Based Discontinuous Reception for Massive Narrowband Internet of Things Systems

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.

UM Paging: Unified M2M Paging with Optimal DRX Cycle

In the long term evolution advanced, a user in idle state can activate the discontinuous reception (DRX) to save its energy, and the 3GPP extends the length of a DRX cycle. Therefore, how to configure the extended DRX (eDRX) cycle is a tradeoff between the tracking area update (TAU) cost (the cost of updating a user’s location) and the paging cost (the cost of waking up a user). In this letter, we analyze the tradeoff and determine the optimal length for the eDRX cycle under the latency constraint. The objective function is to minimize the total signaling cost considering the tradeoff between TAU cost and paging cost. The analytical and simulation results demonstrate that the proposed mathematical model can mitigate the signaling cost for the networks.

Adjustable Extended Discontinuous Reception Cycle for Idle-State Users in LTE-A

Supporting a large number of devices in LTE Machine-to-Machine (M2M) communications is a big challenge. The large number of devices will try to access radio resource in a short period of time which may result in severe congestion to the Radio Access Network (RAN). Hence, how to scatter the devices, which are always trying to access to a same eNBs (evolved Node B), to other eNBs has become an important issue. In other words, balancing the random access intensity among eNBs will reduce the congestion of an RAN and improve the network utilization. Nevertheless, how to select an eNB to attach to during the initial access in a way that will balance overall access intensity in the network is a key challenge. Unfortunately, from the perspective of eNB, an eNB is unaware of how many devices will access to the eNB. Thus, an eNB cannot release the information about the access intensity in advance. In this paper, therefore, we propose four effective eNB selection algorithms to estimate the access intensity of an eNB. Simulation results show that the proposed algorithms perform better than conventional approach.

Scattering Random-Access Intensity in LTE Machine-to-Machine (M2M) Communications

In LTE (Long Term Evolution) femtocell networks, user equipment (UE) has to measure all the eNBs and Home eNBs (HeNBs) in the Measurement Configuration and find a suitable eNB (or HeNB) to hand over to. However, there are many HeNBs under the coverage of one eNB. In other words, the UE has to measure all the eNBs and HeNBs, no matter whether they are in or out of range. In addition, in many urban areas, the indoor signal strength of LTE networks is fairly good due to the dense deployment of eNBs. As a consequence, the source eNB will not ask the UE entering the coverage of an HeNB to perform the measurement procedure because the source eNB can still provide a desirable signal strength. In this case, the UE will still camp on the eNB, and the HeNB is therefore idle. In this paper, we propose an efficient measurement procedure for handover in LTE femtocell networks to solve the two above-mentioned problems. The simulation results show that the measurement overhead and the average idle time are significantly reduced.

Efficient measurement procedure for handover in LTE femtocell networks

Collecting data from a tremendous amount of Internet-of-Things (IoT) devices for next generation networks is a big challenge. A large number of devices may lead to severe congestion in Radio Access Network (RAN) and Core Network (CN). 3GPP has specified several mechanisms to handle the congestion caused by massive amounts of devices. However, detailed settings and strategies of them are not defined in the standards and are left for operators. In this paper, we propose two congestion control algorithms which efficiently reduce the congestion. Simulation results demonstrate that the proposed algorithms can achieve 20&#126;40% improvement regarding accept ratio, overload degree and waiting time compared with those in LTE-A.

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Chia-Wei Chang

Papers

UM Paging: Unified M2M Paging with Optimal DRX Cycle

Adjustable Extended Discontinuous Reception Cycle for Idle-State Users in LTE-A

Scattering Random-Access Intensity in LTE Machine-to-Machine (M2M) Communications

Efficient measurement procedure for handover in LTE femtocell networks

Congestion Control for Machine-Type Communications in LTE-A Networks