Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

This paper proposes a new user association method to maximize the downlink system throughput in a cellular network, where the system throughput is defined based on (p,α)-proportional fairness. The proposed method assumes a fully decentralized approach, which is practical in a real system as complicated inter-base station (BS) cooperation is not required. In the proposed method, each BS periodically and individually broadcasts supplemental information regarding its bandwidth allocation to newly connected users. Assisted by this information, each user calculates the expected throughput that will be obtained by connecting to the respective BSs. Each user terminal feeds back the metric for user association to the temporally best BS, which represents a relative increase in throughput through re-association to that BS. Based on the reported metrics from multiple users, each BS individually updates the user association. The proposed method gives a general framework for optimal user association for (p,α)-proportional fairness-based system throughput maximization and is especially effective in heterogeneous cellular networks where low transmission-power pico BSs overlay a high transmission-power macro BS. Computer simulation results show that the proposed method maximizes the system throughput from the viewpoint of the given (p,α)-proportional fairness.

In this letter, we propose two robust and distributed game-based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [1] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.

The increase in network access devices and demand for high quality of service (QoS) by the users have led to insufficient capacity for the network operators. Moreover, the existing control equipment and mechanisms are not flexible and agile enough for the dynamically changing environment of heterogeneous cellular networks (HetNets). This non-agile control plane is hard to scale with ever increasing traffic demand and has become the performance bottleneck. Furthermore, the new HetNet architecture requires tight coordination and cooperation for the densely deployed small cell base stations, particularly for interference mitigation and dynamic frequency reuse and sharing. These issues further complicate the existing control plane and can cause serious inefficiencies in terms of users' quality of experience and network performance. This article presents an SDN control framework for energy efficient downlink/uplink scheduling in HetNets. The framework decouples the control plane from data plane by means of a logically centralized controller with distributed agents implemented in separate entities of the network (users and base stations). The scheduling problem consists of three sub-problems: (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. Moreover, these sub-problems are coupled and must be solved simultaneously. We formulate the DL/UL scheduling in HetNet as an optimization problem and use the Markov approximation framework to propose a distributed economical algorithm. Then, we divide the algorithm into three sub-routines for (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. These sub-routines are then implemented on different agents of the SDN framework. We run extensive simulation to validate our proposal and finally, present the performance analysis.

This work investigates the cell range expansion (CRE) possible with time-domain multiplexing inter-cell interference coordination (TDM ICIC) in heterogeneous cellular networks (HCN). CRE is proposed to enable a user to connect to a picocell even when it is not the cell with the strongest received power. However, the users in the expanded region suffer severe interference from the macrocells. To alleviate the cross-tier interference, TDM ICIC is proposed to improve the SIR of pico users. In contrast to previous studies on CRE with TDM ICIC, which rely mostly on simulations, we give theoretical analysis results for different types of users in HCN with CRE and TDM ICIC under the Poisson Point Process (PPP) model, especially for the users in the expanded region of picocells. We analyze the outage probability and average ergodic rate based on the connect probability and statistical distance we obtain in advance. Furthermore, we analyze the optimal ratio of almost blank subframes (ABS) and bias factor of picocells in terms of the network fairness, which is useful in the parameter design of a two-tier HCN.

In heterogeneous cellular networks (HCN), which consists of macrocells and numerous femtocells, efficient interference management schemes between macrocells and femtocells are so crucial to the overall system performance. To mitigate intercell interference in HCN, we propose a new rate-split transmission scheme which has the following characteristics. First, it supports user quality of service (QoS) with the least intercell interference. Second, it is a low complexity and distributed scheme using only Interference to Signal and Noise Ratio (ISNR). An evaluation confirms that the proposed scheme offers better performance than legacy schemes which are not considering user QoS.

In heterogeneous cellular networks (HCN), which consists of macrocells and picocells, efficient interference management schemes between macrocells and picocells are crucial to the overall system performance. We propose a dynamic cooperative silencing (DCS) scheme for intercell interference control (ICIC). It is a low-complexity, low-feedback and distributed algorithm using only strongly interfered neighboring user information. A system simulation shows that the system performance and in particular the cell-edge throughput is significantly increased with the proposed silencing scheme.