Youming Sun

A Cloud-Based Architecture for the Internet of Spectrum Devices Over Future Wireless Networks

The dramatic increase in data rates in wireless networks has caused radio spectrum usage to be an essential and critical issue. Spectrum sharing is widely recognized as an affordable, near-term method to address this issue. This paper first characterizes the new features of spectrum sharing in future wireless networks, including heterogeneity in sharing bands, diversity in sharing patterns, crowd intelligence in sharing devices, and hyperdensification in sharing networks. Then, to harness the benefits of these unique features and promote a vision of spectrum without bounds and networks without borders, this paper introduces a new concept of the Internet of spectrum devices (IoSDs) and develops a cloud-based architecture for IoSD over future wireless networks, with the prime aim of building a bridging network among various spectrum monitoring devices and massive spectrum utilization devices, and enabling a highly efficient spectrum sharing and management paradigm for future wireless networks. Furthermore, this paper presents a systematic tutorial on the key enabling techniques of the IoSD, including big spectrum data analytics, hierarchal spectrum resource optimization, and quality of experience-oriented spectrum service evaluation. In addition, the unresolved research issues are also presented.

Energy-Aware Joint User Scheduling and Power Control for Two-Tier Femtocell Networks: A Hierarchical Game Approach

Femtocells are considered as promising technologies for the next generation wireless networks to improve system capacity and enhance indoor coverage. The dense deployment of femtocells brings a lot of challenges in terms of interference management and energy consumption. To alleviate co-channel interference, we investigate interference management based on hierarchically joint user scheduling and power control in downlink femtocell networks considering the impact of energy cost. Then, we formulate this problem as a Stackelberg game with one leader and multiple followers. Specifically, macro base station and femtocell base stations select their optimal scheduled users and determine the optimal transmission power, based on their own net utilities considering power cost. To obtain the Stackelberg equilibrium (SE) in a distributed manner, a hierarchical and iterative user scheduling and power update framework is proposed. Moreover, we prove that the optimization of user-scheduling and power control can be decoupled in our scheme and the proposed scheme converges to a unique SE under mild conditions. Simulation results are presented to show the convergence and effectiveness of the proposed hierarchical game.

An iterative approach for sparse direction-of-arrival estimation in co-prime arrays with off-grid targets

This paper addresses the problem of direction of arrival (DOA) estimation by exploiting the sparsity enforced recovery technique for co-prime arrays, which can increase the degrees of freedom. To apply the sparsity based technique, the discretization of the potential DOA range is required and every target must fall on the predefined grid. Off-grid target can highly deteriorate the recovery performance. To the end, this paper takes the off-grid DOAs into account and reformulates the sparse recovery problem with unknown grid offset vector. By introducing a convex function majorizing the given objective function, an iterative approach is developed to gradually amend the offset vector to achieve final DOA estimation. Numerical simulations are provided to verify the effectiveness of the proposed method in terms of detection ability, resolution ability and root mean squared estimation error, as compared to the other state-of-the-art methods.

Demand-aware resource allocation for ultra-dense small cell networks: an interference-separation clustering-based solution

In this paper, we present a novel clustering-based resource allocation framework for downlink transmission in ultra-dense small cell networks. Specifically, we first model a combinatorial optimisation problem that jointly considers subchannel and power allocation and user traffic demand in terms of a large-scale network scenario. Unfortunately, the huge communication overhead and computational complexity make it challenging for traditional centralised/distributed solutions. To address this issue, we propose an interference-separation clustering-based scheme to divide the massive small cells into smaller groups with different priorities, which reduces the network scale. Different from the existing cluster construction scheme, the proposed clustering method effectively avoids the inter-cluster interference through coordination. Then, for a given cluster configuration, we formulate the distributed resource allocation problem as a local interaction game where the utility of each player comprises not only its own profits but also the interests of neighbours. We prove the existence of Nash equilibrium for the formulated game and design a hierarchical learning algorithm to achieve the Nash equilibrium, which only needs local information exchange. Finally, simulation results validate that the proposed solution outperforms some other existing approaches and is more suitable for large-scale networks. Copyright

Hierarchical Resource Allocation Framework for Hyper-Dense Small Cell Networks

This paper considers joint power control and subchannel allocation for co-tier interference mitigation in extremely dense small cell networks, which is formulated as a combinatorial optimization problem. Since it is intractable to obtain the globally optimum assignment policy for existing techniques due to the huge computation and communication overheads in ultra-dense scenario, in this paper, we propose a hierarchical resource allocation framework to achieve a desirable solution. Specifically, the solution is obtained by dividing the original optimization problem into four stages in partially distributed manner. First, we propose a divide-and-conquer strategy by invoking clustering technique to decompose the dense network into smaller disjoint clusters. Then, within each cluster, one of the small cell access points is elected as a cluster head to carry out intra-cluster subchannel allocation with a low-complexity algorithm. To tackle the issue of inter-cluster interference, we further develop a distributed learning-base coordination mechanism. Moreover, a local power adjustment scheme is also presented to improve the system performance. Numerical results verify the efficiency of the proposed hierarchical scheme, and demonstrate that our solution outperforms the state-of-the-art methods, especially for hyper-dense networks.

VERACITY: Overlapping Coalition Formation-Based Double Auction for Heterogeneous Demand and Spectrum Reusability

Spectrum auction is one of the most effective solutions to allocate the spectrum resource following the market rules and has attracted much attention from both academia and industry. However, most of the existing studies assume that the spectrum buyers demands are homogeneous and the interference relationship is fixed without any change with the variation of spectrum. Furthermore, the economical efficiency of auction outcome has not drawn enough attention. That motivates us to design an auction scheme to jointly consider the multi-demand of buyers, heterogeneous spectrum, and economical efficiency. In this paper, we propose a novel overlapping coalition formation-based double auction, called VERACITY, to address this problem. The auctioneer groups the conflict free buyers into the same coalition and allows a buyer to join multiple coalitions based on the heterogeneous demand. Dynamic overlapping coalition formation implemented by the auctioneer is to find the approximately optimal coalition structure corresponding to the economical efficiency outcome, i.e., maximizing the social welfare. Furthermore, we prove that VERACITY is individually rational, budget balanced, truthful, and economically efficient. Simulation results are presented to show the convergence and effectiveness of the proposed VERACITY.

Distributed Channel Access for Device-to-Device Communications: A Hypergraph-Based Learning Solution

In this letter, we propose a learning solution for distributed channel access in device-to-device communications based on a hypergraph interference model. We first define a new interference metric for a hypergraph model, and then formulate this distributed channel access problem as a local altruistic game, which is proved to be an exact potential game admitting at least one pure strategy Nash equilibrium (PNE). A distributed learning algorithm is designed to quickly achieve the optimal PNE, which can minimize the defined networks’ interference metric. Simulation results show that the proposed algorithm outperforms the existing schemes and significantly improves the spectrum efficiency.

Local altruistic coalition formation game for spectrum sharing and interference management in hyper-dense cloud-RANs

In this study, the authors investigate the spectrum sharing and interference management in hyper-dense cloud radio access networks (C-RANs). The authors formulate this problem as a local altruistic coalition formation game (LACF) with externalities. Different from previous studies considering maximising individual small cells throughput, the authors’ purpose is to maximise the social welfare, i.e. the sum satisfaction of remote radio heads (RRHs) with diverse traffic types. The authors design a local altruistic utility function for RRH, i.e. each RRH cares about not only his own interest but also his neighbours’. They make autonomous decisions, finally shaping the entire RRHs into multiple disjoint coalitions. In addition, joint transmission coordinated multi-point technology is considered within coalition to mitigate interference. Then, the authors propose a distributed coalitional formation algorithm based on modified recursive core to obtain the final stable coalition partition. Furthermore, the system stability, convergence and complexity of the proposed algorithm are analysed. Numerical results show that the proposed algorithm outperforms the classical and non-cooperation schemes in the perspective of both social welfare and individual fairness. To be specific, the average rate per RRH of the authors’ proposed scheme is superior to the classical and non-cooperation cases by 20 and 25% in hyper-dense scenario, respectively.

Energy Efficiency of Small Cell Networks: Metrics, Methods and Market

Although the promising 5G cell network technology has increased the transmitting rate greatly, it has also brought some challenges. The energy efficiency has become an important topic in 5G networks. In this paper, the energy efficiency of small cell networks is analyzed, and the existing objective functions are classified in order to minimize the energy consumption, and to maximize the energy efficiency, harvested energy, and energy-aware transmission. Commonly used metrics were analyzed on equipment, base station, and network levels, respectively. Moreover, the methods for energy efficiency improvement were introduced according to above-mentioned metrics. Afterward, the relationships between energy efficiency, spectrum efficiency, and space efficiency were discussed. In order to improve efficiency on equipment, base station, and network levels, the energy and spectrum market is proposed and guidelines for the future research on metrics, methods, and market are presented. The proposed market was verified by simulations, and the simulation results have shown that the proposed market improves the energy efficiencies effectively.

Opportunistic channel access with repetition time diversity and switching cost: a block multi-armed bandit approach

Abstract In this paper, we investigate the channel access problem considering switching cost in the block fading channels with unknown information of channel occupation and quality. We formulate this problem as a multi-armed bandit (MAB) problem with the goal of minimizing the outage rate and avoiding frequent channel switching. To achieve this goal, a block based multi-armed bandit (BMAB) learning algorithm is proposed. Furthermore, the BMAB algorithm is extended to cope with the short-term deep channel fading, by exploiting the repetition time diversity (RTD). The regrets of the proposed two algorithms are proved to be logarithmic in time. Performance analysis and simulation results show that the proposed algorithms outperform standard SMAB algorithm in average system outage rate, switching cost and throughput. In addition, the repetition time diversity multi-armed bandit (RTDMAB) algorithm is better than BMAB algorithm in the presence of deep channel fading at the cost of receiving complexity .