Tiankui Zhang

Distributed Energy Efficient Fair User Association in Massive MIMO Enabled HetNets

Massive multiple-input and multiple-output (MIMO) and heterogeneous networks (HetNets) have been recognized as key enabling technologies for future fifth generation (5G) mobile networks. However, the circuit power consumption of massive MIMO scales with the tremendous number of antennas. As a result, the problem of energy efficient user association in massive MIMO enabled HetNets is of vital importance. We investigate the energy efficient user association problem in massive MIMO enabled HetNets, and formulate the network logarithmic utility maximization problem. Based on the Lagrangian dual analysis, a low complexity distributed user association algorithm is developed for energy efficient fair user association while considering quality of service (QoS) provision for users. Simulation results demonstrate the effectiveness of the proposed algorithm in improving the energy efficiency and user fairness, compared to other user association algorithms.

Opportunistic User Association for Multi-Service HetNets Using Nash Bargaining Solution

We propose an opportunistic user association for multi-service HetNets aiming to guarantee quality of service (QoS) of human-to-human (H2H) traffic while providing fair resource allocation for machine-to-machine (M2M) traffic. We classify H2H traffic as primary service and M2M traffic as secondary service. The opportunistic user association is formulated as an optimization problem, which can be resolved by Nash Bargaining Solution (NBS). Simulation results show that the proposed algorithm can enable network operators to support fair resource allocation for M2M traffic without jeopardizing QoS of H2H traffic.

Two-Dimensional Optimization on User Association and Green Energy Allocation for HetNets With Hybrid Energy Sources

In green communications, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak on-grid energy consumption, since the large peak on-grid energy consumption will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we consider the two-dimensional optimization to lexicographically minimize the on-grid energy consumption in heterogeneous networks (HetNets). All the base stations (BSs) therein are envisioned to be powered by both power grid and renewable energy sources, and the harvested energy can be stored in rechargeable batteries. The lexicographic minimization of on-grid energy consumption involves the optimization in both the space and time dimensions, due to the temporal and spatial dynamics of mobile traffic and green energy generation. The reasonable assumption of time scale separation allows us to decompose the problem into two sub-optimization problems without loss of optimality of the original optimization problem. We first formulate the user association optimization in space dimension via convex optimization to minimize total energy consumption through distributing the traffic across different BSs appropriately in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. To solve the optimization problem, we propose a low complexity optimal offline algorithm with infinite battery capacity by assuming non-causal green energy and traffic information. The proposed optimal offline algorithm serves as performance upper bound for evaluating practical online algorithms. We further develop some heuristic online algorithms with finite battery capacity which require only causal green energy and traffic information. The performance of the proposed optimal offline and online algorithms is evaluated by simulations.

Energy Efficiency of Base Station Deployment in Ultra Dense HetNets: A Stochastic Geometry Analysis

Ultra dense heterogeneous networks (HetNets), which involve densely deployed small cells underlaying traditional macro cellular networks, will be an enabling solution for extremely high data rate communications. However, the dense deployment of small cell base stations (BSs) inevitably triggers a tremendous escalation of energy consumption. In this letter, we investigate the impact of BS deployment, especially BS density on energy efficiency in ultra dense HetNets using the stochastic geometry theory. The minimum achievable data rate in terms of the traffic load in each tier is characterized, and then the minimum achievable throughput of the whole HetNets is obtained. Finally, the closed-form energy efficiency with respect to the BS deployment is derived. The simulation validates the accuracy of the theoretical analysis, and demonstrates that the energy efficiency maximization can be achieved by the optimized BS deployment.

Research on network coding based Hybrid-ARQ scheme for wireless networks

In a downlink wireless network, the XOR network coding method was used to combine with the Hybrid-ARQ (HARQ) scheme, which can improve the retransmission efficiency of the wireless network. The average number of transmissions and the average packet delay metrics were used to evaluate the performance of the network coding based HARQ (NC-HARQ). The theoretical analysis was given to compare the NC-HARQ scheme with traditional HARQ scheme, both in the broadcast and unicast scenarios. The theoretical analysis and simulation results show that the NC-HARQ can reduce the average number of trans-missions and benefit the retransmission efficiency with some packet delay expense.

Adaptive user association in HetNets with renewable energy powered base stations

In this paper, we propose adaptive user association in the Heterogeneous Networks (HetNets) with renewable energy powered base stations (BSs), where all the BSs are assumed solely powered by the harvested energy from the renewable energy sources. BSs across tiers differ in terms of energy harvesting rate, maximum transmit power and deployment density. In conventional grid-powered HetNets, user association is determined based on the assumption that all the BSs can transmit with constant powers, whereas the transmit powers of BSs vary in the HetNets with renewable energy powered BSs. The adaptive user association is formulated as an optimization problem which aims to maximize the number of accepted user equipments (UEs) and minimize the radio resource consumption in the scenario where the available energy of BSs is dependent on the harvested energy in a certain period of time. We first propose an optimal offline algorithm, where the gradient descent method is used to achieve the pseudo-optimal user association solution. The performance of proposed gradient descent based user association algorithm is verified by simulation results. Considering practical implementation, we further propose a heuristic online user association algorithm which is capable of making timely user association decision for the incoming UEs based on remaining available network resources. Simulation result indicates the proposed online algorithm achieves good tradeoff between UEs acceptance ratio and association delay.

Stackelberg Game Based Cooperative User Relay Assisted Load Balancing in Cellular Networks

We propose a Stackelberg game based cooperative user relay assisted load balancing (LB) scheme to tackle SNR degradation problem of shifted cell-edge users which commonly occurs in a conventional direct handover LB scheme. In the proposed scheme, users from a lightly loaded cell can be selected as cooperative user-relays and will be paid by the shifted cell-edge users. Stackelberg game theory is applied to optimize the strategies of both the user relays and shifted cell-edge users, in order to maximize both of their utilities in terms of SNR and payment. Theoretical analysis and simulation study are undertaken to show the effectiveness of the proposed scheme.

Utility Fair Resource Allocation Based on Game Theory in OFDM Systems

Radio resource allocation is one of the key technologies in orthogonal frequency division multiplexing (OFDM) cellular systems, where subcarriers and power are schedulable resources. In this paper, we solve the fair resource allocation problem based on the idea of the Nash bargaining solution (NBS) from cooperative game theory, which not only provides the resource allocation of users that are Pareto optimal from the view of the whole system, but also are consistent with the fairness axioms of game theory. We develop a suboptimal solution of NBS via low-pass time window filter and first-order Taylor expansion, and then propose an efficient and practical dynamic subcarriers allocation algorithm. Simulation results show that the proposed dynamic subcarriers allocation algorithm providing utility fairness and improving system capacity.

Multi-relay selection schemes based on evolutionary algorithm in cooperative relay networks

In cooperative relay networks, the selected relay nodes have great impact on the system performance. In this paper, a multi-relay selection schemes that consider both single objective and multi-objective are proposed based on evolutionary algorithms. First, the single-objective optimization problems of the best cooperative relay nodes selection for signal-to-noise ratio SNR maximization or power efficiency optimization are solved based on the quantum particle swarm optimization QPSO. Then the multi-objective optimization problems of the best cooperative relay nodes selection for SNR maximization and power consumption minimization two contradictive objectives or SNR maximization and power efficiency optimization also two contradictive objectives are solved based on a non-dominated sorting QPSO, which can obtain the Pareto front solutions of the problems considering two contradictive objectives simultaneously. The relay systems can select one solution from the Pareto front solutions according to the trade-off of SNR and power consumption or the trade-off of SNR and power efficiency to take part in the cooperative transmission. Simulation results show that the QPSO-based multi-relay selection schemes have the ability to search global optimal solution compared with other multi-relay selection schemes in literature. Simulation results also show that the non-dominated sorting QPSO-based multi-relay selection schemes obtain the same Pareto solutions as exhaustive search, but the proposed schemes have a very low complexity. Copyright

Joint user association and green energy allocation in HetNets with hybrid energy sources

In the heterogeneous networks (HetNets) powered by hybrid energy sources, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak-to-average on-grid energy consumption ratio, since the large peak-to-average on-grid energy consumption ratio will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we propose a joint user association and green energy allocation algorithm which aims to lexicographically minimize the on-grid energy consumption in HetNets, where all the base stations (BSs) are assumed to be powered by both the power grid and renewable energy sources. The optimization problem involves both the user association optimization in space dimension, and the green energy allocation in time dimension. The independence nature of this two-dimensional optimization allows us to decompose the problem into two sub-problems. We first formulate the user association optimization in space dimension as a convex optimization problem to minimize total energy consumption via balancing the traffic across different BSs in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. Simulation results indicate the proposed algorithm achieves significant on-grid energy saving, and substantially reduces peak-to-average on-grid energy consumption ratio.