Dual Connectivity Handoff Analysis for ENDC C/U-Plane Split NSA NR5G Networks

Traditional cellular network requires that a user equipment (UE) should associate to the same base station (BS) in both the downlink (DL) and the uplink (UL). Based on dual connectivity (DC) introduced in LTE-Advanced R12, DL/UL decouple access scheme has been proposed, which is especially suitable for heterogeneous networks (HetNets). This paper is the pioneer to take the DL/UL decouple access scheme into consideration and develop a novel resource allocation algorithm in a two-tier HetNet to improve the total system throughput in the UL and ease the load imbalance between macro base stations (MBSs) and pico base stations (PBSs). A model is formulated as a nonlinear integer programming, and the proposed algorithm is a sub-optimal algorithm based on the graph theory. First, an undirected and weighted interference graph is obtained. Next, the users are grouped to let users with large mutual interferences to be assigned to different clusters. Then, the users in different clusters are allocated to different resource blocks (RBs) by using the Hungarian algorithm. Simulation results show that the proposed algorithm can provide great promotions for both the total system throughput and the average cell edge user throughput and successfully ease the load imbalance between MBSs and PBSs.

Heterogeneous Networks (HetNets) have gained the attraction of the communication industry recently, due to their promising ability to enhance the performance of future broadband Fifth Generation (5G) networks and are integral parts of 5G systems. They can be viewed in multi-dimensional space where, each slice represents a unique tier that has its own Base Station (BS)s and User Equipment (UE)s. Different tiers cooperate with each other for their mutual benefit. Data can be interactively exchanged among the tiers, and UEs have the flexibility to switch between the tiers. The cells in such a heterogeneous cellular networks have variable sizes, shapes, and coverage regions. However, in HetNets with ultra dense BSs, the distance between them gets very small and, they suffer from very high levels of mutual interference. To improve the performance of HetNets, we have done multiple contributions in this dissertation. First, we have developed analytical derivations for optimizing pilot sequence length which is a very crucial factor in acquiring the Channel State Information (CSI) and the channel estimation process in general. Poisson Point Process (PPP) has been widely used to allocate BSs among various tiers so far. However, BS locations obtained using PPP approach may not be optimum to reduce interference. Therefore, in this dissertation, BSs locations are optimized to reduce the interference and improve the coverage and received signal power. Also, we have derived expressions for static UEs coverage probability and network energy efficiency in HetNets. A proper UE association algorithm for HetNets is a great challenge. The classic max-Signal to Interference and Noise Ratio (SINR) or max-received signal strength (RSS) user association algorithms are inappropriate solutions for HetNets as UEs in this context will tend to connect to the Macro BS, which is the one with the highest signal power. A severe load imbalance and significant inefficiency arises and impacts the performance. The aforementioned algorithms tend to associate UEs to BSs with the best received signal power or signal quality. In HetNets, usually Macro BSs are the ones transmitting the strongest signals; hence most UEs tend to associate with the Macro BS leaving Micro BSs with less load. Also, the conventional max-SINR and max-RSS algorithms do not provide adequate results in multi-tier systems. We suggest two centralized algorithms, LSTD and RTLB, for an even UE association to provide fair load distribution. However RTLB outperforms LSTD in real time scenarios as it easily and quickly adapts to rapid network changes. Furthermore, we consider the mobility of nodes. We derive coverage probability for moving UEs considering both handover and no handover scenarios. Proposed algorithms are fast enough to associate the moving users to different Micro and Macro BSs appropriately in real time. Our algorithms are proved to be feasible and provide a path towards attainable future communication systems.

The Dual connectivity (DC) technology is a robust solution for Internet service providers (ISP) to evolve their legacy infrastructures with less cost to speed up the progress of fifth-generation (5G) services coverage. DC enables the traditional network to connect to multiple New Radio (NR) Node B (gNBs). A well-known scheme is the non-standalone (NSA) NR. In this case, the user equipment (UEs) could access the network via both the evolved Node B (eNB) and the gNB. However, some research has indicated that the UEs in such a configuration suffer from power consumption problems. UEs are typically configured with discontinuous reception (DRX) mechanism to save power. However, most current systems do not investigate and optimize the DRX parameters. To the authors’ best knowledge, this work is the first to combine DRX with traffic steering for the UEs in the NSA NR system. A traffic steering algorithm is proposed to improve the coordination between the eNB and the gNB. We used a semi-Markov chain model to analyze power consumption and average delay. Compared with standard DRX operated in LTE, NR, and DC, we verified that the proposed model achieves power-saving gains without incurring an additional delay. The results indicated that the UE could save considerable power with an optimized system configuration.

The dual connectivity is emerging as a promising solution to boost capacity in heterogeneous networks. However, it is challenging to obtain an optimal user association in heterogeneous networks with dual connectivity, due to its non-convex and combinatorial nature. In this paper, we propose a user association scheme based on deep reinforcement learning to maximize the overall network utility, which takes both throughput and user fairness into account, in the downlink of a heterogeneous network. Particularly, each user associates with the macro base station (BS) and a micro BS. We apply a deep Q-network (DQN) to obtain the nearly optimal policy to associate the users and micro BSs. Simulation results demonstrate that DQN-based user association performs better compared to the conventional user association schemes in heterogeneous networks with dual connectivity, and it behaves good scalability when the environment changes.

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

To meet the subscribers ever increasing traffic demand, micro and macro base stations are being deployed excessively. As the traffic pattern varies according to the user's behavior, the deployment of micro and macro base stations plays a vital role in saving energy while maintaining the traffic demand of the subscribers. A macro base station consumes more than double the energy than a micro base station. Due to the space and time characteristics of the traffic, the BS cannot allocate resources effectively, which results in wasting energy consumption and low energy efficiency. Therefore, energy saving through deployment of base stations play a significant role to increase the energy efficiency. In this paper, the user traffic pattern is determined and the resources needed to fulfill the traffic are analyzed and finally the deployment strategies for the base stations are formulated.
 Since the base stations are fully loaded only for few hours a day, energy saving on the stations during low traffic will be significant. The energy saving schemes saved up to 18.8 % of energy in macro and 26.9 % of energy in micro BS. So, it would be more efficient to implement a heterogeneous network with more micro cells with energy saving schemes than just macro base stations.

The 3rd generation partnership project (3GPP) has recently specified the non-standalone 5G New Radio (NR), in which the dual connectivity (DC) feature plays an essential role. A User Equipment (UE) with DC is able to concurrently connect to an evolved NodeB (eNB) (e.g., using LTE in a macro cell) and a next generation NodeB (gNB) (e.g., using 5G NR in a small cell) aiming to satisfy the 5G's required Key Performance Indicators (KPIs). In this work, we focus on the issue of concurrent transmissions on the 5G small cell, which potentially exploit the benefits of DC. With DC, an application is able to spread its traffic over different two paths (via eNB and gNB) between the UE and the LTE core network. The traffic over diverse paths may experience different delay since the conditions are generally different (e.g., due to wireless channel, background traffic). Therefore, one of the most important problems in efficient operation with DC is minimizing the end-to-end delay considering all the connections. To address that problem, we adopt the deterministic network calculus to first characterize the delay experienced by each connection; then to model the overall end-to- end delay in the 5G small cell. We then formulate an optimization problem that attempts to find the optimal traffic splitting over the connections while minimizing the delay. We derive the solution for the problem, as well as, present the numerical results.

Interference alignment (IA) in heterogeneous networks (HetNet) is a promising technology that can achieve a significant increase of network capacity. However, there are some issues on IA in HetNet. One of them is that each pico base station (BS) requires a large number of transmit antennas, because it tries to align all inter-pico interference. In actual environments, the strength of inter-pico interference varies depending on the distance between pico cells, i.e., inter-pico interference from the remote pico cell is much smaller than that from the close one, so that aligning all interference is not necessarily the best. In addition, the residual interference from macro BS that is not eliminated by IA may decrease the capacity. In this paper, we propose 1) to reduce the required number of transmit antennas at pico BS by clustering pico cells based on the strength of inter-pico interference and 2) to apply combined weight, that comprises ZF (zero forcing) weight and MMSE (minimum mean square error) weight, to pico user equipment (UE) so as to mitigate the residual interference that is not eliminated perfectly by IA. Simulation results show that by applying combined weight to pico UEs, the capacity increases compared to the case of applying only ZF weight, and that the average rate per pico cell normalized by the required number of transmit antennas at each pico BS, that is, the rate per transmit antenna can be increased with pico cell clustering, which indicates that each pico BS can utilize its transmit antennas more efficiently.

The first phase of new radio (NR) standard, which was finalized in December 2017, is called a non‐standalone (NSA) NR as it is based on dual connectivity between an LTE radio access technology (RAT) and the NR RAT, known also as E‐UTRA – NR Dual Connectivity(EN‐DC). This chapter presents an overview of 5G core (5GC) network and functionalities by presenting the specified logical network nodes which are the standardized building blocks for the 5GC and by giving an example of a signaling flow between the nodes for connection establishment. It discusses the different dual connectivity options and related terminology. Two different protocol stacks exist in NR system: control plane protocol stack, carrying control information between user equipment (UE) and gNB or Core Network, and user plane protocol stack, carrying data between different applications in the UE and in the network.

In Heterogeneous networks (HetNets), the power difference between macro base stations (MBSs) and small base stations (SBSs) causes severe load unbalance. Therefore, cell range expansion (CRE) is proposed as an effective method to extend the coverage of SBSs and achieve balanced utilization of BSs. However, the downlink (DL) quality for offloaded user equipment (UE) cannot be guaranteed. In this paper, a traffic-aware user association scheme is proposed in HetNets. Distinct association biases are applied to different UEs according to their requirements. System performance of the proposed scheme is analyzed using the tool of stochastic geometry. The results show that the proposed scheme can improve DL throughput by enhancing the rate coverage of UEs, meanwhile signal-to-interference-plus-noise ratio (SINR) requirement with low data rate demand UEs is ensured. Moreover, the optimal association bias, which maximizes DL throughput, can be derived through particle swarm optimization (PSO), and it changes with different densities of BSs and UEs.

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.

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.

Dividing the transmission power statically among Long Term Evolution (LTE) and New Radio (NR) carriers during dual connectivity may result in a great deal of unused power, which may lower the throughput and user experience. RAN4 has mandated the need for Dynamic Power Sharing (DPS) for all the User Equipments (UEs) supporting LTE-NR dual connectivity. DPS allows the UE to distribute the transmit power among carriers. However, optimizing the power distribution among the LTE and NR carriers during dual connectivity efficiently and effectively is still an open problem. In this paper, we propose a novel distributed Q-learning assisted DPS scheme to learn the distribution of transmission powers for LTE and NR carriers for dual connectivity. Extensive simulations and analysis reveal that our proposed scheme improves the network performance by allocating transmission powers optimistically considering channel fluctuations.

The expansion of the Internet of Things (IoT) has led to a significant surge in data flow over edge networks, posing substantial challenges to data mining and management. While federated edge learning (FEEL) effectively accomplishes global integration and local training based on the decentralized data sets, its deployment across expansive IoT networks introduces additional challenges. The primary issues stem from managing the interaction between the communication load and learning effectiveness. The communication loads driven by recurrent data exchanges between the user equipment (UE) and central servers exacerbate network congestion and latency issues. Moreover, the learning efficacy is undermined due to the typically nonindependent and identically distributed (non-IID) characteristics of real-world IoT data. In this article, a novel communication-efficient hierarchical FEEL framework is proposed to tackle these challenges. Specifically, UEs are adaptively clustered according to their link conditions, geographic locations, and data distributions. Small base stations (SBSs) collect local model updates from the UEs in their clusters and communicate with a macro base station (MBS) for the global model aggregation. To jointly maximize the communication gain (in terms of reducing latency) and the learning gain (in terms of improving accuracy), a clustering and resource allocation optimization problem is formulated, and a cross entropy-based method with low computational complexity is proposed. Numerical experiments validate that the proposed hierarchical FEEL system achieves fast convergence and significantly improves the system efficiency for various learning tasks and the system settings.

In Band Full Duplex (IBFD) is an emerging radio technology that can double Spectral Efficiency (SE), one of the key enhancement item for 5G. Due to complex processing and hardware required to realize IBFD capability, it is generally not recommended for User Equipments (UEs), while the Base Stations (BSs) can accommodate these changes. This results in Hybrid IBFD Cellular Network (HICN) that restricts IBFD capability strictly to BS and continues with legacy Half Duplex (HD) UEs. Due to this IBFD capability mismatch, frequency allocation is a challenging task in HICN and, to the best of our knowledge, a generic allocation algorithm to maximize sum-SE of a cell and work across different cell sizes is not reported yet in literature. In this paper, we propose the frequency allocation algorithm for HICN that maximizes sum-SE of a cell and shown to work independent of cell size. An adaptive and robust UE grouping algorithm is introduced that, with the knowledge of location information of UEs, shares frequencies among UEs based on their distance separation. To characterize the performance of the proposed algorithm, we have used frequency reuse efficiency (i·) that signifies the amount of frequency reuse and sum-SE for the accumulated SE achieved by all UEs in UL and DL. We have obtained closed-form analytical expression for i· as well as bounds on number of frequencies required. Simulation results reveal that the frequency reuse is doubled with increase in number of UEs, leading to doubling sum-SE for Macro, Micro and Pico BS classes.