Mobility management in HetNets: a learning-based perspective

The use of small cell deployments in heterogeneous network (HetNet) environments is expected to be a key feature of 4G networks and beyond, and essential for providing higher user throughput and cell-edge coverage. However, due to different coverage sizes of macro and pico base stations (BSs), such a paradigm shift introduces additional requirements and challenges in dense networks. Among these challenges is the handover performance of user equipment (UEs), which will be impacted especially when high velocity UEs traverse picocells. In this paper, we propose a coordination-based and context-aware mobility management (MM) procedure for small cell networks using tools from reinforcement learning. Here, macro and pico BSs jointly learn their long-term traffic loads and optimal cell range expansion, and schedule their UEs based on their velocities and historical rates (exchanged among tiers). The proposed approach is shown to not only outperform the classical MM in terms of UE throughput, but also to enable better fairness. In average, a gain of up to 80% is achieved for UE throughput, while the handover failure probability is reduced up to a factor of three by the proposed learning based MM approaches.

HetNet networks are one of the key features of LTE and the ones beyond. Despite the development created in HetNet, recent studies have shown that HOFs and PPs can cause serious problems given the small size of the cell. On these networks, given the difference in base station's coverage, the handover performance of the user equipment (UE) may significantly reduce; this is especially obvious in situations where UE passes through the small cells at a high-speed. In this paper, a context-aware mobility management (MM) approach is presented for small-cellular networks, where reinforcement learning. BSs collectively learn their long-term traffic loads and optimal extension of their cell range and schedule their UE according to the speed and rate of history data, along with the consideration of QOS, which is exchanged over rows. The proposed method not only provides better performance than MM in terms of power, but also provides better fairness conditions that reduce the likelihood of a Handover and Ping Pong failure. In the survey, macro and picocells learn how to optimize their longterm traffic, whereas in the short term, the UE communication process is based on history and timing based on speed and QOS. Thus, a long-term load balancing approach aims to improve overall system capacity while reducing the probability of Hand Over Failure (HoF) and Ping Pong (PP).

Ultra dense network of small cells (SCs) was introduced to support high data rate services and increase network capacity. Owing to user equipment (UE) mobility and limited coverage of SCs, the load across a SC network recurrently becomes unbalanced. Such unbalanced loads result in performance degradation of network in terms of throughput, inefficient utilization of network resources and link failure. This paper proposes and evaluates a novel load balancing methodology based on UE-SC association, which incorporated two main factors:(i)load of SCs in UE’s neighborhood (ii)the received Signal to Interference and Noise Ratio (SINR)/Channel Quality Indicator (CQI) and Quality of Service (QoS) requirements of user. The proposed algorithm ensures that the standard deviation of load in SCs in the neighborhood of a given UE remains minimum when the UE is associated to a potential SC. Consequently, achieving load balancing among SCs proactively while maintaining high SINR/CQI levels for UE, with each UE-SC pairing. System-level simulations show that the proposed algorithm provides a more balanced load across network, which is quantified by a smaller standard deviation of load across the SCs and higher network throughput when compared to other load balancing algorithms.

Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.

Mobility management with the central-based location area policy

In recent years, there has been a tremendous increase in the cellular traffic due to the availability of wide range of devices: smart phones, net-books, tablets, etc. The existing cellular networks will be unable to cater to the increasing demands in near future, thus we need technological enhancements in the cellular infrastructure to meet the ever increasing user requirements. Various approaches have been suggested to increase the existing cellular capacity and provide higher data rates, some of which include deployment of small cells under the coverage area of Macro cells, where a cell denotes the region covered by a particular base station (BS). However, since these small cells cover small regions there exists a significant handover signalling overhead. We suggest an approach where small cells, called Phantom cells, are deployed within the Macro cell coverage area, and the existing Macro BS functions as the centralized controller for all the Phantom BSs deployed within its range. Phantom BSs act as a supplement to the existing Radio Access Network in the LTE infrastructure where it handles the data plane (D-plane) and Macro BSs handle the control plane (C-plane). This paper proposes the definitions of Copiane and D-plane, the modifications in the user equipment (UE) protocol stack which enable the concurrent operation with dual BSs (Phantom and Macro). Mechanisms are developed for the communication of a Phantom BS with a Macro BS over the new interface (X3 interface). NS-3 simulations were performed incorporating the designed architecture for Phantom based HetNets and a significant improvement in UE throughput is observed in comparision with legacy networks comprising of Macro BS.

This paper analyzes the problem of optimum user association and sum rate maximization for software defined radio access networks (SD-RANs) with access node diversity for fifth generation (5G) wireless networks. We consider four complementary types of access nodes namely, a massive multiple- input multiple-output (MIMO) base station (BS), MIMO BSs, small cells (SCs), and indoor and outdoor distributed antenna systems (DAS). The SDRAN user association problem is solved through a novel three-stage optimization scheme called the place coverage (TPC). TPC divides the user equipment (UEs) into two sets of indoor and outdoor UEs. Initially, TPC associates indoor UEs with the indoor DAS access nodes. Next, outdoor UEs are associated with the outdoor DAS access nodes, SCs, MIMO BSs, and the massive MIMO BS. Finally, the remaining resources of indoor and outdoor access nodes are used to serve the UEs that have not been served. The scope of TPC is multifold. First, TPC reflects the closest model to a real-world diverse 5G network. Secondly, servicing the indoor UEs with iDAS antennas results in lower power load per user for the access network resulting in better coverage, quality, and data speed. Thirdly, TPC provides reduced radiation levels for indoor UEs. Numerical results show that TPC provides significant sum rate and fairness gains over the received signals strength (RSS) based association schemes [1]. Moreover, TPC provides comparable performance levels as the optimum association scheme based on exhaustive search while bringing significant complexity reduction.

To overcome latency constrain of common mobile cloud computing, computing capabilities can be integrated into a base station in mobile networks. This exploitation of convergence of mobile networks and cloud computing enables to take advantage of proximity between a user equipment (UE) and its serving station to lower latency and to avoid backhaul overloading due to cloud computing services. This concept of cloud-enabled small cells is known as small cell cloud (SCC). In this paper, we propose algorithm for selection of path between the UE and the cell, which performs computing for this particular UE. As a path selection metrics we consider transmission delay and energy consumed for transmission of offloaded data. The path selection considering both metrics is formulated as Markov Decision Process. Comparing to a conventional delivery of data to the computing small cells, the proposed algorithm enables to reduce the delay by 9% and to increase users' satisfaction with experienced delay by 6.5%.

Differentiated quality-of-Service (QoS) techniques are widely used to distinguish between different service classes and prioritize service needs in mobile networks. Mobile Network Operators (MNOs) utilize QoS techniques to develop strategies that are supported by the mobile network infrastructure. However, QoS deployment strategy can ensure that the radio resources provided by the base station are easily consumed if it is not used correctly or when different techniques are used all together. In this paper, we propose a scheduling algorithm and compare two different QoS deployment strategies for prioritized User Equipment (UEs) (with higher scheduling rates and dedicated bandwidth) that MNOs can use in the current infrastructure, using a commercial real-time Long Term Evolution (LTE) network in different test scenarios. Moreover, we expose the real-time user experience in terms of uplink throughput and analyze results of the UE’s real-time key performance indicators (KPIs) in detail. Experiment results are evaluated considering the implications of different QoS support types on network coverage and capacity planning optimizations. Our results demonstrate that even though pre-configured resource allocations can be given to prioritize UEs, the experience of all the UEs can be affected unexpectedly in the presence of many UEs who have received different QoS deployment support. Our experimental observations have revealed that location of the UEs with respect to Base Station (BS) and the availability of dedicated bandwidth UEs inside cell may have implications on the apriori defined resource allocation strategies of the other UEs.

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics.

This paper presents the link-level performance of downlink non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) receiver, considering the influence of error vector magnitude (EVM). In our evaluations, for the case of a 1-by-2 transmission and the number of NOMA multiplexed users is two, we clarify the performance of NOMA for cell-center user equipment (UE) with codeword level SIC (CWIC) receiver and the performance of the cell-edge UE, which does not apply SIC. Based on link-level performance evaluations, we show that the performance of CWIC receiver is almost the same as that of ideal SIC when the allocated transmit power ratio to cell-center UE is less than 0.35, the modulation and coding scheme (MCS) of cell-center UE is 16QAM (R = 0.51) or 64QAM (R = 0.47), and the MCS of cell-edge UE is QPSK (R = 0.51). Furthermore, we also show that, when EVM value is 4 % and the allocated power ratio to cell-center UE is between 0.2 and 0.37, the level of degradation due to the influence of EVM is below 0.6 dB, which is almost the same level of degradation as that of orthogonal frequency division multiple access (OFDMA) for the EVM value required by 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE). On the other hand, we show that the influence of EVM is marginal for cell-edge UE compared to cell-center UE. However, when the cell-center UE uses a higher order modulation such as 64QAM, we show that the required SNR of cell-edge UE increases due to the increase in inter-user interference. Based on these results, we clarify that it is important for the base station (BS) to take into account the target BLER and influence of EVM when selecting the MCS and allocated transmit power ratio of NOMA UEs.

This paper presents possibility of dense small cell network for local areas from a view of network control considering two types of applications; one is 100m cells for a major road supporting vehicles' mobility and another one is 10m cells for campuses supporting walking mobility. They adopt 3GLTE protocol after slight modification. Handover is directly triggered from a UE (User Equipment) to a target BS (base station) to shorten the handover period though it is relayed on a serving BS in conventional systems while drawing large latency due to backhaul communications. The direct request uses SR (Schedule Request) signal on PUCCH (Physical Uplink Control Channel) of 3G-LTE protocol. This can be allowed by short propagating delay in a small cell and synchronous operation of all BSs under clock of PON (Passive Optical Network) used for the backhaul network. Handover resources like SR codes, downward frames, and terminal ID are reserved among the target UE and a group of BSs next to one serving the target UE before handover. Cells surrounding the target UE follow the move of the UE always confining the target UE in the cells with the reserved handover resources.

Handover (HO) algorithms, managed by Mobility Management Entity (MM), and Scheduling algorithms, managed by Radio Resource Management Entity (RRM) are both the most important aspects in the next-generation cellular systems 3GPP Long Term Evolution (LTE). When a User Equipment (UE) is in motion, the constraints of offering better Quality of Service (QoS), especially in Real Time applications, increase. This is why the design of a powerful handover algorithm is essential, in order to ensure a seamless transfer of a UE to a new cell. In this article, we propose a new HO algorithm based on the Available Bandwidth prediction at the eNBs level. The improvements proposed by our scheme compared to other HO algorithms such as LTE Hard Handover Algorithm (LHHA), Integrator Handover and LTE Hard Handover Algorithm with Average RSRP Constraint (LHHAARC) has been shown via different parameters namely throughput, system delay and Packet Loss Ratio (PLR). Using LTE-Sim simulator, all HO algorithms were simulated against the three well-known scheduling schemes, namely, Proportional Fair (PF), Exponential Proportional Fairness (EXP / PF) and Modified Largest Weighted Delay First (MLWDF). The objective is to define a synthesis allowing the automatic choice of the proper scheduling scheme to use, according to the HO decision algorithm predefined by the system.

In this paper, we investigate the situation where the base station (BS) with a wider bandwidth communicates with the multiple user equipments (UEs) of a narrower bandwidth. In the Long Term Evolution (LTE) systems, although the bandwidth of the BS is large enough to contain multiple carrier components (CCs) for the UEs, the BS are not allowed to generate wide baseband signals because the LTE UEs are not designed to extract their CCs from such signals. To take the advantages of wideband operation, we propose a unified baseband signal generation method which covers multiple CCs. The proposed method is related to compensating the phases which are caused by the difference in center frequencies between BS and UEs. In order to reduce computation complexity, we also suggest an efficient technique calculating the phase values for the compensation process.

Deploying pico cell and femto cell nodes within a macro cell layout is known as heterogeneous networks. It is a promising solution to enhance cell-edges coverage with the improvement of the overall system performance. The handover performance to user equipment is technically challenging especially when user’s equipment velocity increases and traverse towardsthe pico cells. To overcome this problem, Multi Armed Bandit learning, based on Bayesian theory and entropy measures have been proposed. User’s equipmentlearns the best cell based on the posterior distribution of reward and continuous optimal cell range expansion value is predicted through linear regression. Entropy is used to evict the user equipment among macro cell and pico cell. The potential reward on each base stations channel is calculated andthe channel with the maximum accumulated rewards is formally chosen. Gain up to 86 % is achieved for user equipment throughput, while the handover failure probability is reduced by a factor of 4.