Deep Learning Based User Association in Heterogeneous Wireless Networks

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user's traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes.

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.

User association is a crucial factor that affects the performance of wireless networks. In current cellular networks, user association is typically coupled, which means an user equipment (UE) must associate with the same base station (BS) in uplink (UL) and downlink (DL). For single-tier wireless networks, such mechanism is simple and effective. However, in heterogeneous ultra-dense networks (UDNs), there are distinct differences in transmission power, data traffic and channel quality etc., for which coupled association could restrict the performance of system. To cope with it, the concept of decoupled UL-DL (DUDe) association has been introduced recently, which enables a UE to associate with different BSs in UL and DL. In this paper, we investigate decoupled UL-DL user association in UDNs. Considering the existence of asymmetric information (i.e., channel gains and intercell interferences), which can be seen as the private information for UE, we propose a contract-theoretic user association approach. Particularly, we model the decoupled association process as a monopoly labor market, where BSs act as employers and offer contracts to employees (i.e., UEs). The contract items cover the available associated bandwidths, transmitted powers and corresponding prices. Then BS broadcasts these drafted contract information, and UE selects to sign the optimal contract by considering her own demands. Numerical results show significant superiorities of DUDe than coupled UL-DL association in perspective of nodes utilities and social surplus, and compared with the existing user association methods, contract-theoretic approach shows a certain improvement in performance.

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.

SummarySmall cell networks can fulfill the increasing demandfor the high data rate of wireless applications. Energy efficiency is an important design parameter of the ultra dense small cell network (UDSCN). The sleeping strategy of small base stations (s‐BSs) is used to enhance the network's energy efficiency. An efficient sleeping strategy of s‐BSs is required while preserving users' quality of service (QoS). The idle s‐BSs can be switched to sleep mode. This paper proposes a blockchain‐enabled solution for the sleeping strategy of s‐BSs. Here, a blockchain‐enabled small cell network is created between the s‐BSs. The network is decentralized, which eliminates the workload of the macro base station (MBS). The proposed network architecture is enabled as a decentralized network through blockchain. The blockchain provides distributed control over the s‐BS operations through a smart contract. Here, smart contracts act as distributed self organizing network features to handle self‐transactions among small cells for switching off s‐BSs in the network. All the software logic required to perform s‐BS operations is written in a smart contract using Ethereum. The proposed solution improves energy efficiency and enables the ultra dense small cell network to be decentralized.

To alleviate the backhaul congestion in future hybrid heterogenous networks, this paper investigates the potential benefits of implementing storages in small base stations (SBSs) operating at frequency range 2 (FR2) bands that co-exist with a tier of massive multiple input multiple output (MIMO) macro BSs (MBSs) operating at frequency range 1 (FR1) bands. We develop a unified analytical framework and derive theoretical bounds for such a cache-enabled FR1-FR2 hybrid network under limited backhaul scenario to analyze the exact and approximate latency, average success probability of file delivery, and average data rate considering two open-access user association policies: i) location-based and ii) content-based association. Numerical results demonstrate that wireless edge caching (WEC) can improve the performance of hybrid wireless networks, albeit certain trade-offs, e.g., increasing cache-enabled SBSs cannot always improve the network performance and there exists an optimal SBS density that provides the best latency and throughput performance. Furthermore, we compare the performance of the network with respect to other key network design parameters such as cache size, content popularity, backhaul capacity, and blockages for both the user associations. Our results show that latency under content-based user association is less than that of location-based user association, and although the difference in the average rates under the two user associations is not obvious, content-based association can extricate more backhaul capacity and thus reduce installation cost significantly.

We introduce a sleep mechanism for energy efficiency in heterogeneous cellular networks, where macro base stations and small base stations coexist in the same frequency band. The strategy is to put high-power macro base stations into sleep mode and offload the users to low-power small base stations or neighboring macro base stations. To do so, we first formulate a joint optimization problem to minimize the total energy consumption while maintaining the QoS of users. Then we decouple it into two subproblems: user association and resource allocation, and macro base station sleep mechanism. We propose a modified many-to-one matching algorithm to solve the first one and a voting-based dynamic sleep mechanism for the second one. Simulation results show that our scheme has both lower energy consumption and lower user blocking ratio compared with existing sleep mechanisms.

In this paper, we propose MIU, a novel mobile edge computing framework for iot applications in ultra dense networks, via the control of the macro base station. We present a comprehensive framework model, and formulate a joint task offloading, user association and small base station sleeping problem, aiming at minimizing the energy consumptions of network-wide IoT devices and total SBSs while respecting a series of practical constraints. We design an efficient algorithm by invoking dual-decomposition and subgradient method to solve the formulated mixed-integer quadratic programming problem. Extensive simulation results show that our proposed algorithm achieves better performance in energy consumption than several benchmark schemes.

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.

In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy-, and infrastructure-efficient communications. Each IoT device transmits its own information by backscattering the signals from the primary network without using active radio-frequency (RF) transmitter chain. We consider the symbiosis between the cellular network and the IoT network and focus on the user association problem in SRN. Specifically, the base station (BS) in the primary network serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem. To overcome this issue, we propose two deep reinforcement learning (DRL) algorithms, both use historical information to infer the current information in order to make appropriate decisions. One algorithm, referred to as centralized DRL, makes decisions for all IoT devices at one time with globally available information. The other algorithm, referred to as distributed DRL, makes a decision only for one IoT device at one time using locally available information. Finally, simulation results show that the two proposed DRL algorithms achieve performance comparable to the optimal user association policy which requires perfect real-time information, and the distributed DRL algorithm has the advantage of scalability.

A new method about renewable energy cooperation among small base stations (SBSs) is proposed, which is for maximizing the energy efficiency in ultradense network (UDN). In UDN each SBS is equipped with energy harvesting (EH) unit, and the energy arrival times are modeled as a Poisson counting process. Firstly, SBSs of large traffic demands are selected as the clustering centers, and then all SBSs are clustered using dynamic k-means algorithm. Secondly, SBSs coordinate their renewable energy within each formed cluster. The process of energy cooperation among SBSs is considered as Markov decision process. Q-learning algorithm is utilized to optimize energy cooperation. In the algorithm there are four different actions and their corresponding reward functions. Q-learning explores the action as much as possible and predicts better action by calculating reward. In addition, ε greedy policy is used to ensure the algorithm convergence. Finally, simulation results show that the new method reduces data dimension and improves calculation speed, which furthermore improves the utilization of renewable energy and promotes the performance of UDN. Through online optimization, the proposed method can significantly improve the energy utilization rate and data transmission rate.

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.

This paper considers a heterogenous wireless cellular network (HetNet) where many small base stations (SBS) coexist. SBSs can be deactivated and put to sleep to save energy and are equipped with two power sources, harvested energy (HE) and a grid power source, where an SBS will use its available HE to serve the associated users first. Then, the SBS will request any shortage of its energy from other active or deactivated SBSs that have a surplus of HE. Finally, if there is still an energy shortage, the SBS will use power drawn from the grid. This transfer of energy is facilitated through the use of the promising smart grid (SG)technology. We investigate the grid energy minimization problem by optimizing both the transmission power and activation/deactivation (dynamic sleeping) of the SBSs. However, since the formulated problem is a mixed integer nonLinear problem (MINLP), generalized Benders decomposition (GBD) is used to decompose the problem into two subproblems: user association and energy harvesting which are solved iteratively. Further, a new heuristic approach is proposed that provides a computationally efficient algorithm to solve and optimize the user association and energy harvesting problems of the system model. This approach uses network centrality to develop a measuring parameter, base station centrality (BSC), of SBS centrality in the network. BSC is presented to mark the SBSs that have the most potential to be deactivated without affecting the quality of service (QoS) of users. Finally, extensive simulations are performed to verify the superiority of the proposed BSC-based strategy over GBD in terms of operational cost.

This paper investigates multicast beamforming for scalable videos in cache-enabled heterogeneous networks, where a macro base station (MBS) and multiple small base stations (SBSs) serve multicast group users on-demand video services. Inspired by the main idea of the scalable video coding, the extension technology of the H.264#x002F;advanced video coding, each video is divided into a base layer (BL) and an enhancement layer (EL). The BL can provide the fundamental viewing quality and adding EL to the received BL can guarantee superior perceptual experience. The MBS and multiple SBSs cache the BLs and ELs of the most popular videos, respectively. Employing the transmission schemes in coordinated multi-point, i.e., parallel transmission and joint transmission, the MBS and SBSs parallelly transmit BLs and ELs while the SBSs cooperatively transmit the ELs if higher video qualities are required. To improve the quality of the received videos for users whose requirements can be satisfied locally, our aim is to maximize the weighted sum rate of them and this problem can be converted into an iterative second-order cone programming problem by successive convex optimization with low complexity. Numerical results demonstrate the advantage of our proposed scheme compared to the benchmark scheme, i.e., only MBS serving the end users, even when the transmit power of each SBS is kept low.