User-centric Ultra-dense Networks Research Articles

Load distribution of base stations in user-centric heterogeneous UDN

In ultra-dense networks (UDN), multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations (BSs). The data rate and quality of service can be improved. However, BSs in user-centric paradigm are required to serve more users due to this multiple association scheme. The improvement of system performance may be limited by the improving load of BSs. In this letter, we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN. Based on open loop power control (OLPC), a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio (SINR) for any typical user. As for any BS in different tiers, by leveraging stochastic geometry, we derive the Probability Mass Function (PMF) of the number of the served users, the Cumulative Distribution Function (CDF) of total power consumption, and the CDF bounds of downlink sum data rate. The accuracy of the theoretical analysis is validated by numerical simulations, and the effect the system parameters on the load of BSs is also presented.

Joint Resource Allocation Optimization for the Wireless Backhaul Link of User-Centric Cell-Free NOMA Networks

In this paper, we investigate the downlink resource allocation problem for the user-centric ultra-dense networks (UUDN) which can provide high area throughput density. UUDN can effectively improve the user experience since one user can be served by more than one access points (APs) simultaneously. However, the ultra-dense node deployment will also result in serious co-channel interference which will degrade the performance of the networks. Nonorthogonal multiple access (NOMA) is introduced to reduce the interference caused by the network densification. We consider the resource allocation for both the wireless backhaul downlink and the user access downlink at the same time in UUDN with NOMA, which involves the APs grouping and power allocation. The resource allocation problem is formulated as a mixed integer nonconvex optimization problem due to the combinatorial nature of APs grouping. We first decompose the problem into the APs grouping subproblem and power allocation subproblem to reduce the computational complexity. For the APs grouping subproblem, we model it as a bipartite graph maximum weight matching problem, and the Hungarian algorithm is used to solve it. When the APs grouping is fixed, the original problem is still difficult to tackle since the power allocation is a max-min optimization problem. The KKT conditions are applied to optimize the power allocation subproblem. Simulation results illustrate that the proposed resource allocation algorithm can effectively improve the system throughput of UUDN.

Secure and Reliable Downlink Transmission for Energy-Efficient User-Centric Ultra-Dense Networks: An Accelerated DRL Approach

Due to the inherent openness of wireless channels, data transmission in user-centric ultra-dense networks (UUDNs) is vulnerable to eavesdropping and malicious jamming attacks (EJA). This paper addresses the user-centric clustering, beamforming, and artificial noise design to combat with the EJA, and an accelerated deep reinforcement learning (DRL) based approach has been proposed for the downlink of UUDNs from a security, reliability and energy efficiency (SREE) perspective. In contrast to the existing approaches, the proposed approach divides the UE's communication behavior into association phase and transmission phase to perform a fast and adaptive response to the EJA. For the association phase, the proposed approach aims at achieving a fast response to EJA by optimization algorithms from a short-term perspective. For the transmission phase, we propose a DRL based algorithm to achieve optimal adaptive response by dealing with the dynamic and persistent effects of EJA from a long-term perspective. To accelerate the training process, fast response achieved in the association phase will be regarded as the initial experience to update the parameters of the deep neural networks adopted by the DRL based algorithm for transmission phase. Comparing with the existing methods, simulation results show that our proposed accelerated DRL approach achieves better performance in terms of SREE and convergence speed.

A proactive selection method for dynamic access points grouping in user-centric UDN

Network densification is a promising solution to fulfill network capacity requirement and transmission rate for beyond 5G and 6G wireless communications. Ultra-dense network (UDN) integrates heterogeneous network resources and coordinates technologies on quality of service controlling, to provide users with flexible service. However, dense deployment reduces coverage radius of the cell, resulting in an increase on handover frequency, which makes a serious impact on service continuity. In this paper, we propose a proactive selection method for dynamic access points grouping (DAPGing) in accordance with "user-centric" philosophy, which selects target Access Points (AP) and reduces handover times to ensure communication continuity. This method includes two criteria: 1) the user's sojourn time, which is determined by analyzing the AP coverage area; 2) neighbor relationship between APs, which is determined by coverage area and signal strength characteristics between neighboring APs. Therefore, candidate APs become the proactive selected ones to update the AP group. Stochastic geometry is used to build system model and performance metrics are analyzed, including AP group coverage probability and average update frequency. Experimental analysis shows that the proposed proactive selection method brings similar coverage probability to traditional handover method, while average update frequency is reduced more than 20% selection criteria.

User Access and Resource Allocation in Full-Duplex User-Centric Ultra-Dense Networks

User-centric ultra-dense network (UUDN) has become a promising paradigm with the advantages over traditional cell-centric structure with respect to spectrum and power usage. This paper introduces full duplex (FD) technology into UUDN and presents a joint scheme of user access, subchannel allocation and power control for FD UUDN. The scheme firstly judges the feasibility of the constraints of the transmit power and rate requirements. If they are feasible, the total network capacity is maximized under the transmit power constraints. Otherwise, the minimal total power of the network is found to satisfy the rate requirements. The sum rate maximization is solved by multiplicative linear fractional programming (MLFP) based power allocation (MAPEL) algorithm and particle swarm optimization (PSO) algorithm, respectively. The power minimization problem is transformed by averaging the total rate in each subchannel of the user, and then solved by time-sharing method. Simulation demonstrates the enhancement of spectrum efficiency and the reduction of consumed power for UUDN compared with cell-centric UDN. Moreover, the advantage of FD over half duplex (HD) is shown in terms of spectrum efficiency and power consumption.

Performance Analysis for Uplink Transmission in User-Centric Ultra-Dense V2I Networks

User-centric ultra-dense network (UDN) has been considered a key enabler of high data rate and high reliable vehicle-to-infrastructure (V2I) communication. In this paper, an analytical framework is developed based on stochastic geometry to characterize the uplink V2I transmission performances, in terms of the outage probability, the average vehicle throughput and the average spatial throughput, particularly resolving two important but not well-understood factors in such networks: the spatial correlation in the interference powers among the cooperative roadside units (RSUs) as caused by the shared set of co-channel vehicles, and the combined vertical distance (CVD) owing to the lane width and the antenna height difference between the vehicle and the RSUs. Exact analytical expressions of the outage probability are derived for the general multiple association cases, a far-distance approximation method is introduced to reduce the computational complexity, and lower and upper bounds are obtained corresponding to two extreme assumptions of the spatial interference correlation. We show that the impacts of the spatial interference correlation can be significant and ignoring the correlation can lead to non-optimal system deployment, and that the far-distance approximation can approach well with the exact results. Meanwhile, although CVD diminishes the V2I uplink transmission performances, the diminution can be mitigated through RSU cooperation.

Resource allocation for energy efficient user association in user-centric ultra-dense networks integrating NOMA and beamforming

A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Thenwe solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.

Lightweight Batch AKA Scheme for User-Centric Ultra-Dense Networks

Ultra-dense networks (UDN) are considered as one of the key technologies for advancing the widespread deployment of 5G networks. To provide continuous and reliable connectivity to user devices and to achieve the integrity and authenticity of communication, UDN needs to address the challenges related to authentication and authorization of information. Existing authentication and key agreement (AKA) schemes are not appropriate for the ultra-dense deployment of access points due to excessive authentication and/or computation overhead. This paper proposes a novel lightweight batch authentication and key agreement (LBAKA) scheme for user-centric UDN scenarios by adopting lightweight batch authentication solution and one-to-one key agreement, and by using mutual authentication to verify the credibility of both sides of the communication. The proposed LBAKA can maintain privacy of identity information under replay, impersonation and man-in-the-middle attacks in user-centric UDNs. In terms of computation and communication overhead, our simulation results show that LBAKA outperforms other similar schemes in user-centric UDNs.

Heuristic Access Points Grouping for Mobility Driven User-Centric Ultra Dense Networks

In ultra-dense network (UDN) scenarios the big number of users and dense placement is comparable to those of the Access Points (APs). In such UDNs new approaches for mobility management are necessary to ensure a reliable service provision, seamless connectivity and high unit area throughput. Dynamic access point grouping for user service provision is considered to be a core function of the UDN with user centric wireless access. In this paper, a heuristic approach with low computational complexity for AP grouping for mobility driven user-centric UDN is proposed, based on two general metrics reflecting the user density and distribution, user requirements and available resources in the APs. This allows the AP grouping process and user association to one or a group of APs to be represented as a dynamic service provision system and its performance as a function of user allocation, user mobility and user service requests to be more effective.

Study on Network Security Based on PCA and BP Neural Network Under Green Communication

In response to the energy consumption caused by the exponential growth of mobile network capacity demand, the Chen Shanzhi team proposed a user-centric ultra-dense network. It dynamically organizes multiple access nodes into a group of access nodes centered on the user. It can “accompany” user services without perception. The result is increased system capacity and user experience. This paper systematically analyzes the key security issues of user-centric ultra-dense networks. Based on the security characteristics of the access node group, a security system adapted to the user-centered ultra-dense network architecture is designed. Aiming at the problem of data security transmission between network entities, a lightweight data security transmission algorithm based on implicit certificate is proposed. The algorithm uses a lightweight implicit certificate to generate a temporary session key by means of a reconfigurable public-private key pair, so as to encrypt and protect the transmitted data, and solve the problem of data security transmission when the access nodes cooperate. The simulation shows that the algorithm consumes 34kB less on average than the traditional key method in different scenarios, and the speed is fast and the encryption key remains stable. At the same time, it also saves storage space and can be conveniently applied to access nodes with limited resources. Then, aiming at the network information security problem, an information security detection method based on improved BP neural network based on PCA is proposed. Simulation experiments were carried out on four types of attack types using the KDD CUP data set. The simulation results show that the improved BP neural network classifier combined with PCA has higher performance in network training. The false alarm rate for the DOS, R2L and PROBE attack types dropped to about 10%, and the false alarm rate for the U2R attack types dropped by 8.07%.

Machine Learning Inspired Codeword Selection For Dual Connectivity in 5G User-Centric Ultra-Dense Networks

In future 5G user-centric ultra-dense networks (UUDN), demands of high data rate and high spectrum efficiency are effectively met by dual connectivity (DC) technology. However, due to huge increase of base stations (BSs) and mobile users (MUs), it becomes difficult for BSs to quickly and precisely select the codeword and provide DC to MUs. Hence, different from some traditional methods, this correspondence paper aims to improve the network performance using the method of machine learning. First, we model the random distribution of BSs by homogeneous Poisson point processes, where each MU is served by millimeter-wave channel. Second, the probabilities that macro cell BS or small cell BS serves the MU are further derived to get the average sum rate (ASR) in UUDN. Third, inspired by ML, we utilize an iterative support vector machine (SVM) classifier to select the codewords of BSs, with sequential minimal optimization (SMO) algorithm used for training all link samples in UUDN. Then, an iterative SVM-SMO classification algorithm is proposed to achieve a highly efficient performance of DC, where the convergence and complexity are also discussed. The sample training and simulation at last are evaluated by Google TensorFlow. The simulation verified that our proposed algorithm gets a higher ASR than the traditional channel estimation based algorithm. In addition, the results also show a lower computational complexity can be achieved by the proposed algorithm as well.

User Centric Content Sharing Based on D2D Cellular Networks

To meet the rapid growth of massive user access requirement and service experience rate, user-centric ultra-dense networks are proposed to serve each user seamlessly based on dynamic access point groups, which breaks through the traditional network-centric cellular architecture. However, a large number of access points deployment and the long transmission distance between users and access points make the network cost and delay increase. Therefore, in this paper, a user-centric content sharing mechanism for device-to-device (D2D) cellular networks is proposed by multi-device-to-device (MD2D) user cooperation, which can not only shorten the content transmission distance, but also guarantee the quality of service at the lowest cost. In particular, in order to ensure quality of service at the lowest cost, a noncooperative game-based D2D cooperative user allocation scheme is proposed. However, the allocation scheme does not take into account the conflicts of interest between users because the allocated D2D cooperative users may meet the service requirement of multiple users at the same time. Accordingly, a Vickrey–Clarke–Groves based user trustworthy auction mechanism is proposed to avoid interest conflicts caused by the above-mentioned reasons. The results show that the proposed mechanism not only reduces the consumption and delay of content transmission, but also greatly help the base stations reduce base station load.

Tractable Mobility Model for Multi-Connectivity in 5G User-Centric Ultra-Dense Networks

With the network densification, frequent handovers may degrade mobility reliability, cause heavy signaling load thus blocking the improvement of network capacity. This paper aims at reducing cost in terms of handover in dense networks, deriving compact expressions of network performance metric, including handover probabilities as well as the mobility-aware downlink data rate, under a multi-connectivity mobility model in user-centric ultra-dense networks (UUDNs). Specifically, the nearest $M$ access points (APs) in mobile user’s vicinity serve the user for data transmission, while the best one among them is for control, which reduces handover probability and network overheads without a loss in the throughput performance gain. Based on the scheme, handover probabilities are derived for arbitrary movement trajectory in the UUDN, where the locations of APs are modeled as a homogeneous Poisson point process, and then is included in handover cost to derive the downlink rate for mobile users. It is shown that the handover probability in control-plane can considerably be decreased by at least 30% compared with the traditional single connectivity network. What is more, the downlink rate can be improved significantly by $2\times $ compared with single connectivity.

Grouping and Cooperating Among Access Points in User-Centric Ultra-Dense Networks With Non-Orthogonal Multiple Access

A user-centric ultra-dense network (UUDN) is proposed as one of the promising solutions to provide very high area throughput density and flexible access service for users in the fifth-generation systems. On the one hand, network densification provides opportunities to cooperate among a large number of access points (APs) for serving a given user. On the other hand, the limited radio resources cause the serious competition among numerous APs and may degrade the network performance. Therefore, to support large number of connections and break through the restriction of limited frequency resource, non-orthogonal multiple access (NOMA), which supports multiple signals to transmit on the same frequency resource, is introduced into the UUDN. However, NOMA with network densification arises a series of challenges. And the method to group APs efficiently on the same frequency to support for a given user is a critical problem. Thus, in this paper, we propose a user-centric access framework for providing efficient access service and the flexible resource management in NOMA-based UUDN. Under the proposed framework, we then investigate the access scheme that organizes multiple APs into respective AP group (APG) cooperatively to provide access service for each user, aiming at maximizing the system energy efficiency. First, considering the users’ requirement and network environment, a grouping evaluation model is set up to organize APG efficiently. Then, we formulate the resource allocation problem of APG as a mix-integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem and propose low-complexity algorithms based on matching and differ of convex programming theories to obtain a feasible solution. Extensive simulation results are presented to demonstrate the significant performance improvement compared with the existing schemes.

Security architecture and scheme of user‐centric ultra‐dense network (UUDN)

AbstractUser‐centric ultra‐dense network (UUDN) is an inspiring approach of 5G, characterized by serving each user with dynamic access point group and designed to be independent of user's involvement. Contrast with traditional network‐centric cellular network, the UUDN architecture is based on the philosophy of network serving user. However, there is little systematic analysis of the security of UUDN, especially, the UUDN security architecture and key technologies challenges. After analysis of the main security challenges and requirements for the UUDN network architecture, we propose a new security architecture, details of its design, and the key security technologies for the UUDN security solutions and give performance evaluation for simulation. Finally, we discuss the future works in the fields of UUDN security.

A Social-Energy Based Cluster Management Scheme for User-Centric Ultra-Dense Networks

With the exponential growth of traffic demand, ultra-dense networks are proposed to increase the network capacity. However, the high-density access point (AP) deployment will increase the complexity of AP coordination, and AP cluster (APC) needs to be considered in practical implementations. Due to the dynamic changes in spatiotemporal distribution of users and service demand, we propose a social-energy-based cluster management (SECM) scheme in order to reduce APC update frequency. Specifically, in social domain, we propose a congeniality-based personalized recommendation (CPR) algorithm to predict users' incoming requests. We further propose a CPR-based AP cluster algorithm to solve the matching problem among users, APs, and content. In energy domain, we propose an inter-cluster energy cooperation scheme to avoid the shutting down of members in AP cluster and reduce the update of clusters. Numerical results demonstrate that our proposed scheme can achieve a gain of 77.8% in the APC management utility averagely, without loss of fairness compared with the other state-of-the-art schemes.

User-centric ultra-dense networks for 5G: challenges, methodologies, and directions

Ultra-dense networking (UDN) is considered as a promising technology for 5G. In this article, we define user-centric UDN (UUDN) by introducing the philosophy of the network serving user and the de-cellular method. Based on the analysis of challenges and requirements of UUDN, a new architecture is presented that breaks through the traditional cellular architecture of the network controlling user. Dynamic AP grouping is proposed as the core function of UUDN, through which a user could enjoy satisfactory and secure service following her movement. Furthermore, we provide methods for mobility management, resource management, interference management, and security issues. We point out that these functions should be co-designed and jointly optimized in order to improve the system throughput with higher resource utilization, better user experience, and increased energy efficiency. Finally, future works in UUDN are discussed.