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In heterogeneous network, it is important to mitigate cross-tier interference. Resource partitioning is the one of solution to reduce interference. However, most of studies on partitioning assumed that there was only one network operator to cooperate with. In this letter, we study the network selection problem of small access points in heterogeneous networks, which are provided by multiple network operators. We model the multiple network operators scenario as a congestion game. To solve the equilibrium point of suggested game, we analyze some features of proposed model such as potential game property, smoothed best response dynamics and logit equilibrium. Then, we propose a reinforcement learning algorithm that can reach logit equilibrium in distributed way. Moreover, we also suggest the adjustment of learning parameters to enhance adaptability. By means of simulations, it is shown that proposed algorithm has near-optimal performance in view of throughput, fairness and adaptability. [ABSTRACT FROM AUTHOR]

In this paper, we consider a dynamic time division duplex system based on a cloud radio access network (RAN) to support the ultimate goal of efficient resource utilization. To optimize the performance of both remote radio heads (RRHs) and user equipments (UEs) which have multiple antennas, we propose an iterative scheme consisting of four types of beamforming, namely downlink (DL) transmit beamforming, DL receive beamforming, uplink (UL) transmit beamforming, and UL receive beamforming, in an attempt to minimize mean squared error (MSE). The proposed beamforming scheme makes it possible to allow DL and UL transmission be supported simultaneously by managing interference between RRHs and UEs. Our simulation results show that the MSE and the bit error rate (BER) are greatly improved under the proposed beamforming algorithm compared to conventional schemes. From intensive system level simulations, the proposed algorithm points out more than 3 dB performance improvement in terms of the required signal-to-noise-ratio (SNR) to achieve the BER less than $10^{-3}$ compared to conventional schemes. [ABSTRACT FROM AUTHOR]

In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted sum rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner, which possibly requires long computation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method of managing a semidynamic cluster through the use of a reinforcement learning. We derive some concepts from reinforcement learning that could be suitable for cooperative networks. We also verify the performance of proposed algorithm by means of a simulation, in which we examined spectral efficiency and convergence properties. The proposed algorithm represents a considerable improvement for edge users in particular. In addition, we analyze the complexity of the clustering schemes. Our proposed algorithm is effective in the environment where there is a limited computational resource. [ABSTRACT FROM AUTHOR]

In this paper, we propose a non orthogonal multiple access (NOMA) system using coexisting narrow and wide beams to increase total capacity. In our proposed system, a base station considers the state of user mobility and supports users with either a narrow or a wide beam according to the mobility state. The proposed NOMA system uses the beam gain between a narrow and a wide beam, as well as the power gain from the pathloss difference in NOMA scheme. The system is thus less influenced by user mobility at the point of NOMA pairing. In the proposed system, we consider two power allocation schemes, namely the max and fair power allocation methods, and propose a pairing algorithm to maximize the capacity by minimizing the interference between narrow and wide beam user. The result verifies that the proposed system improves sum throughput, compared with the OMA and conventional NOMA system. [ABSTRACT FROM AUTHOR]

The problem of spectrum scarcity has raised the prospect of coexisting network. For example, long-term evolution-licensed-assisted access (LTE-LAA & Wi-Fi) can operate in the same frequency band. However, interference between the two different radio access technologies can severely reduce the performances of both networks. To mitigate this interference, the concept of an almost blank subframe (ABS) has been proposed. However, because the LTE-LAA macrocell must remain silent, this provides no incentive for its throughput enhancement. In this letter, we propose a transaction between LTE-LAA and Wi-Fi using ABS together with offloading. We model a time-resource utility model by jointly considering the resource consumed for ABS and offloading, and then formulate a coalition game. To achieve the solution of a coalition game, we consider two fairness concepts: min-max and Shapley fairness. We then verify the performance of the proposed game using simulations. It is shown that the proposed game model can improve resource utilization through the joint use of ABS and offloading. [ABSTRACT FROM PUBLISHER]

We herein consider directional transmission based on adaptive beamforming in which multiple vehicles are served at the same time using multiple directional beams; this represents one promising technology that could improve the capacity of vehicle-to-infrastructure (V2I) communication. We propose an iterative resource-allocation method to maximize the capacity of V2I communication by taking into proper account the interference between directional beams, which reduces capacity. We also consider a heuristic resource allocation scheme, which has significantly less computational complexity than the iterative scheme and negligible performance degradation. We make use of computer simulations to show that the capacity of V2I communication can be improved using our proposed schemes. [ABSTRACT FROM PUBLISHER]

Recently, self-healing has been actively investigated for mitigating an unforeseen network failure. In particular, to enable self-healing operations in indoor wireless communications systems, an autonomous mechanism to resolve unforeseen network failure problems should be considered. Therefore, we here address the issue of autonomous self-healing, in which continuous connectivity can be provided to users by resolving unexpected network failures. To overcome this problem, we propose a low-complexity resource allocation algorithm based on an optimization approach with reduced message passing. In the proposed algorithm, normal base stations perform subchannel and power allocations with a minimum amount of information sharing (NH(M-1)) to provide reliable service to users in faulty cells autonomously. We also show that the proposed algorithm converges to a unique fixed point in the low-interference region by using a contraction mapping technique. Through simulation results, we demonstrate that the proposed algorithm achieves good performances with respect to the average cell capacity, user fairness, and outage probability while reducing the message passing overhead and computational complexity. [ABSTRACT FROM PUBLISHER]

The need for a new version of a next-generation wireless communication system to handle coinstantaneous downlink (DL) and uplink (UL) traffic is increasing as the asymmetry in DL and UL traffic increases and the traffic flows become more variable. We here propose a DL transmit beamforming algorithm for DL radio remote heads (RRHs), a UL receive beamforming algorithm for UL RRHs, and a power allocation algorithm for UL users. These algorithms are designed to maximize the energy efficiency of a dynamic time division duplex (TDD) based cloud radio access network (C-RAN) system that simultaneously supports DL and UL transmission. Simulation results show that the system developed is more energy efficient to a remarkable degree than conventional algorithms. [ABSTRACT FROM PUBLISHER]

Wi-Fi networks are widely deployed for provision of Internet-centric data services. Since the inception of the Wi-Fi network in 1997 with its technical specification rooted in the IEEE 802.11 standard, much progress for higher data throughput has been made. Currently popular IEEE 802.11n Wi-Fi network in 2.4/5 GHz can deliver 600 Mb/s over a 40 MHz channel, which works well for most types of Internet-centric data services, and a later version of a Wi-Fi network based on IEEE 802.11ac is able to transmit at about 7 Gb/s. A simple configuration of a Wi-Fi network consisting of an AP and multiple stations for bidirectional data transmission enables low-cost implementation. High data rate provided at low cost as well as the abundance of Wi- Fi-capable mobile stations recently led to dense deployment of Wi-Fi networks, particularly in residential areas, business offices, and indoor/ outdoor hotspots. However, dense deployment of Wi-Fi networks (e.g., Wi-Fi DenseNets) causes significantly increased overall interference, and as a result a significantly lowered achievable data rate. Thus, it is sensible to consider technologies that can resolve or mitigate deteriorated throughput of Wi-Fi DenseNets. In this article, technologies to deal with throughput enhancement of Wi-Fi DenseNets are addressed from three different perspectives: exploiting cellular technology for data transmission, elevating spectral efficiency, and controlling overall interference levels. Evaluation of interference control for Wi-Fi DenseNets is carried out in this article, and it is found that significant per-node throughput enhancement can be achieved. [ABSTRACT FROM PUBLISHER]

We compare the capacity of two most popular methods for acquiring the state of the spectrum in cognitive radio technology: geolocation-database-based schemes and spectrum-sensing-based schemes. For the comparison, we use a new Hidden Markov Chain based channel model, because recent measurements show that a conventional two-state model, which has been widely used, is not appropriate for modeling the behavior of channels in the cognitive environment. We also consider more generic cognitive environments in which each wireless system has its own licensed bands and uses unlicensed bands in addition to its licensed bands to increase its capacity. This type of wireless systems can comprise conventional cognitive radio systems by letting the number of licensed bands be zero. Moreover, we have derived the optimal number of unlicensed bands to be used for maximizing the capacity of wireless system by taking into account interference from neighboring wireless systems. Through simulations, we compare the capacity of wireless systems with two channel state acquisition schemes and show the counterbalancing relation between two schemes, which has never been investigated in previous works. To the best of our knowledge, this is the first work to compare the capacity of these two schemes for acquiring the state of the spectrum. [ABSTRACT FROM PUBLISHER]

For the purpose of automating network management, self-organizing network (SON) technology is currently being investigated. There are three important issues in SON: self-configuration, self-optimization, and self-healing. This paper focuses on self-healing, in order to resolve the problem of unexpected network faults and improve network throughput simultaneously. To deal with this problem, we propose a healing channel selection and a cooperative beamforming-based iterative resource allocation algorithms. We utilize the cooperative beamforming in the healing channel based on phase pre-adjustment, and this cooperative beamforming can be performed without power cooperation between distributed nodes. Moreover, we derive the sub-optimality and convergence of the proposed algorithm in weak interference condition by using contraction mapping. Finally, simulation results demonstrate that the proposed algorithm improves average cell capacity and user fairness while repairing network failure effectively. [ABSTRACT FROM PUBLISHER]

We focus on the self-healing of abrupt network faults in a self-organizing network based indoor system. In order to enable self-healing, we design a healing channel and propose a fairness-aware cooperative resource allocation algorithm. Through intensive simulations, we show that the proposed algorithm efficiently supports users fairly in a faulty cell as well as normal cells with little degradation in view of system capacity. [ABSTRACT FROM PUBLISHER]

Femtocells are a promising technology for handling exponentially increasing wireless data traffic. Although extensive attention has been paid to resource control mechanisms, for example, power control and load balancing in femtocell networks, their success largely depends on whether operators and users accept this technology or not. In this paper, we study the economic aspects of femtocell services for the case of monopoly market, and aim to answer questions on operator's revenue, user surplus, and social welfare by considering practical service types and pricing strategies. We consider three user subscription services, that is, users can access only macro BSs (mobile-only), or deploy femto BSs in their house and open / exclusively use their femto BSs (open- / closed-femto). For pricing strategies, flat pricing and partial volume pricing are exploited. The main messages include the following: 1) open-femto service is beneficial to both users and providers; 2) in flat pricing, the impact on operator revenue of allowing or blocking the access of mobile-only users to open femto BSs is minor; and 3) compared with partial volume pricing, flat pricing is advantageous to the operator when users are sensitive to price. [ABSTRACT FROM AUTHOR]

In this letter, we focus on intercell fairness in OFDM femtocell networks. Serious degradation of any cell cannot ensure fairness for entire network, thus, intercell fairness is important for the satisfaction of all users. In order to improve intercell fairness, we devise a cooperative resource-allocation algorithm, which is an enhanced modified iterative water-filling (EMIWF). The proposed algorithm takes account into cell capacity and traffic load at the same time in power allocation. So, each femto base station allocates power in order to reduce the interference that it causes to a femtocell which experiences severe capacity degradation or has heavy traffic load. Through intensive simulations, we show that the EMIWF enhances intercell fairness with negligible degradation in view of system capacity. [ABSTRACT FROM PUBLISHER]