Search

Small cells are introduced to cellular systems to enhance coverage and improve capacity. Densely deploying small cells can not only offload the traffic of macrocells but also provide an energy- and cost-efficient way to meet the sharp increase in traffic demands in mobile networks. However, such a cell deployment paradigm also leads to heterogeneous network (HetNet) infrastructure and raises new challenges for cell planning. In this paper, we study the cell planning issue in the HetNet. Our optimization task is to select a subset of candidate sites to deploy macro or small cells to minimize the total cost of ownership (TCO) or the energy consumption of the cellular system while satisfying practical constraints. We introduce approximation algorithms to cope with two different cell-planning cases, which are both NP-hard. First, we discuss the macrocell-only case. Our proposed algorithm achieves an approximation ratio of O(\log R) in this scenario, where R is the maximum achievable capacity of macrocells. Then, we introduce an O(\log \widetildeR)-approximation algorithm to the small-cell scenario, where \widetildeR is the maximum achievable capacity of a macrocell with small cells overlaid on it. Numerical results indicate that the HetNet can significantly reduce the TCO and the energy consumption of the cellular system. [ABSTRACT FROM PUBLISHER]

Deploying femtocells underlaying macrocells is a promising way to improve the capacity and enhance the coverage of cellular systems. However, such a two-tier network also gives rise to a cross-tier and intratier interference issue that should be properly addressed to acquire the potential performance gain. In this paper, we study the resource-allocation (RA) problem in a two-tier orthogonal frequency-division multiplexing (OFDM) access-based heterogeneous cellular network, where the femtocells that employ a closed access strategy are equipped with a cognitive radio (CR) function to identify radio environment so that they can share subchannels with the macrocells without generating excessive interference to the macrocell users (MUs), which fall into the coverage of the femtocells. We formulate an optimization task to maximize the sum throughput of the femtocell users (FUs) under the consideration of imperfect spectrum sensing and channel uncertainty while controlling the interference to the MUs below their bearable thresholds in the sense of probability. We introduce a conservative convex approximation to the formulated problem and develop a fast algorithm to solve it by exploiting its structure. Simulation results show that our proposal can improve the throughput of the FUs with almost no changes in the infrastructure of the cellular network. [ABSTRACT FROM PUBLISHER]

Heterogeneous networks (HetNets), where small cells are deployed within the coverage of macrocells, can increase capacity and enhance coverage of a cellular system. Compared with macrocells, small cells have superiority in terms of low installation and operation cost because of their small physical size and low transmission power. Hence, HetNets are deemed as a cost-effective way to address the everlasting radio spectrum crisis. On the other hand, HetNets also unfold a new paradigm from the viewpoint of cellular network planning. In this paper, we study the budgeted cell planning problem in HetNets, where our aim is to maximize the number of traffic demand nodes whose required rates are fully satisfied with a given budget. Our optimization task is challenging, and the formulated problem is hard to solve because of constraints in practical cellular systems, including power limitation, available bandwidth, and traffic requirements. We develop an approximation algorithm that yields an (e-\mbox1)/\mbox2e fraction of the optimum, which not only provides quality-guaranteed solutions to the budgeted cel planning problem but sheds useful lights on how to plan a HetNet with limited capital expenditure as well. Preliminary numerical results show that small cells can improve the capacity of a cellular system significantly if they are properly planned. [ABSTRACT FROM PUBLISHER]

Antenna selection (AS) is a signal processing technology that can greatly reduce the hardware complexity of multi-antenna systems. Specifically, AS can decrease the number of required radio frequency chains by activating only a subset of the available antennas in each transmission slot. However, optimal AS suffers from a high computational complexity that increases exponentially with the scale of the antenna array. In this paper, we propose a low-complexity AS algorithm based on multi-label learning (MLL), where a deep neural network is employed to determine the set of selected antennas for a given channel matrix. Specifically, the MLL network combines deep canonical correlation analysis and an autoencoder in a unified network structure, which can extract the low dimensional features of channel matrix as well as the interdependency among selected antennas, so as to achieve an accurate prediction of the set of selected antennas with a relatively small-scale learning model. Simulation results show that, in comparison with the convex relaxation based method, our proposed MLL-based method can achieve comparable capacity with significantly reduced computation time. [ABSTRACT FROM AUTHOR]

To meet the rapid growth of diverse traffic demand in mobile networks, unmanned aerial vehicles (UAVs) are proposed to support terrestrial networks by carrying base stations towards demand when needed. However, how to deploy the UAVs effectively and efficiently is a challenging task. In this paper, we investigate this problem from the load balancing perspective. Our goal is to make the traffic loads of UAVs almost equal so that the networks can be stable and robust to unexpected events. In the pre-processing stage, we introduce a clustering method to divide users into several categories so as to initialize the positions of UAVs in the maximal local density areas. Then, we propose a user association strategy to make the UAVs serve almost equal traffic demand, where our optimization task is to minimize the maximum traffic demand of subregions with constraints on the capacity and the shape of subregions. Third, we propose a local search procedure to relocate UAVs based on backtracking line search to refine the load balance among the system. By alternately invoking user association and location algorithms, we can obtain (near) optimum UAV positions. Finally, we adjust the altitude of each UAV to decrease power consumption of the system. Numerical results indicate that our proposal can serve more users compared with the SINR-based strategy. Moreover, the traffic capacity among UAVs and the areas of subregions of UAVs are more balanced. [ABSTRACT FROM AUTHOR]

Caching at base stations (BSs) can enhance the performance of a mobile network, which has gained much attention in the past few years. In this paper, we investigate the energy-saving issue in the cache-enabled mobile network, where we try to minimize the total system power consumption with limited radio resource and storage capacity of the BSs. We decouple the formulated difficult optimization task into a series of tractable subproblems and develop efficient algorithms to solve them with reasonable computation load. The key idea of our proposed scheme is to associate as many as possible users with the BSs that have stored the users’ requested files in their caches while considering the spectrum and power budgets of the BSs. Numerical results show our proposed scheme can significantly reduce the system power consumption compared with others. [ABSTRACT FROM AUTHOR]

Cloud radio access networks (C-RANs) have been proposed to provide high energy-spectral efficiency and high data rate at low cost. In this correspondence paper, we introduce a novel C-RAN deployment scheme that is complementary to traditional cellular radio networks. The key idea is to install the remote radio heads (RRHs) of the C-RAN in a load-balanced way to lighten the transmission burden of the backhauls between the RRHs and the centralized baseband unit pool, and to activate the RRHs on demand so as to reduce system-wide energy consumption. Numerical results show that our proposed method performs well in practical scenarios. It provides QoS-guaranteed performance with relatively low capital expenditure and operating expenditure. [ABSTRACT FROM AUTHOR]