Semi-cooperative game theoretic framework for resource allocation in cognitive cellular networks

In cognitive radio networks, secondary users need to access the licensed spectrum while neither disturbing the transmissions of the primary users nor introducing much interference to other secondary users. Hence, proper power controlling is of paramount importance to ensure efficient system operation. The previous work on power control in cognitive networks mainly focuses on the interactions among a set of secondary users. The primary users' influence is rarely considered. In this paper, a new power control game model is formulated, by including the primary users into the player set of the game. The primary users are rewarded for sharing their licensed spectrum with secondary users by setting a reasonable interference cap for the secondary transmissions. However, we ensure a minimum required Quality of Service (QoS) for primary users by severely penalizing their utility if their required transmission quality is not met. Simultaneously, the secondary users achieve energy efficient transmissions while not introducing much interference to both the primary and other secondary users. We prove that the proposed cognitive power control game has a unique Nash equilibrium (NE). The numerical examples show that the proposed game theoretical power control algorithm can provide the secondary users with energy efficient transmissions and the primary users with reasonable monetary rewards, while not compromising their required QoS.

An LTE-Advanced (LTE-A) Heterogeneous Network (HetNet) constitutes of a macro cell and small cells, where, the former acts as an overlay network and the latter as an underlay network. In HetNet, enabling Carrier Aggregation (CA) usually results in severe downlink interference at small cells from macro cell because of high powered transmissions from macro cell on over-lapping component carriers (co-channels). The intensity of downlink interference is much more profound in HetNet enabled with CA than compared with a single carrier HetNet and ultimately affects the Quality of Service (QoS) of users connected to small cells. Some frequency domain solutions like cross carrier scheduling, soft frequency reuse, and downlink power control have been proposed so far, but they do not provide complete orthogonal transmissions among macro and small cells, leading to adjacent channel interference. Also, a few time domain approaches like Almost Blank Subframe (ABS), dynamic ABS have been proposed in the literature. However, the resource utilization of cells decreases due to high number of ABS assignments in the dense deployment, which also results in low user throughput. In this paper, we propose a combined time domain and frequency domain novel idea for downlink interference control, which is via adaptive Discontinuous Reception (DRX) settings through Multi-Objective optimizations and an optimal carrier allocation scheme, respectively. The proposed idea mitigates the downlink co-channel interference by enabling intermittent data reception at the User Equipment (UE) through adaptive DRX settings, also it utilizes an effective carrier allocation scheme such that users are always allocated to carriers with least interference. The analytical model and simulation results reveal that the proposed idea achieves average gain of 3.5 times w.r.t cell throughput when compared with well-known downlink interference mitigation schemes.

Cognitive radio technology is proposed to improve spectrum utilization efficiency through exploiting the unused portions of spectrum in an opportunistic manner. In cognitive radio networks (CRNs), primary users (PUs) are allowed to lease out their unused spectrum to share with cognitive users (CUs). In a CRN consisting of multiple PUs and CUs, the spectrum pricing and resource allocation scheme have to be optimally designed, so that the efficient spectrum sharing among PUs and CUs can be supported and the QoS requirements of both PUs and CUs can be guaranteed. In this paper, we consider the joint spectrum pricing and resource allocation problem of a CRN with imperfect channel condition, i.e., channel state information of the PUs is not fully accessible to the CUs. To characterize the willingness of PUs to share subchannels and the intention of CU on accessing subchannels, a pricing scheme for both PUs and CUs is designed. Addressing the competition of CUs in channel accessing and power allocation, a non-cooperative Game model is formulated. Through solving the Nash equilibrium solution of the Game model, the optimal pricing and resource allocation strategy can be obtained. Numerical results demonstrate the efficiency of the proposed algorithm.

With the rapid development of wireless services and applications, the currently radio spectrum is becoming more crowded. How to accommodate more wireless services and applications within the limited radio spectrum becomes a big challenge faced by modern society. Cognitive radio (CR) is proposed as a promising technology to tackle this challenge by introducing secondary users (SUs) to opportunistically or concurrently access the spectrum allocated to primary users (PUs). Currently, there are two prevalent CR models: the spectrum sharing model and the opportunistic spectrum access model. In the spectrum sharing model, the SUs are allowed to coexist with the PUs as long as the interferences from SUs do not degrade the quality of service (QoS) of PUs to an unacceptable level. In the opportunistic spectrum access model, SUs are allowed to access the spectrum only if the PUs are detected to be inactive. These two models known as underlay and overlay schemes, respectively. This thesis studies a number of topics in CR networks under the framework of these two schemes. First, studied cognitive radio transmissions under QoS delay constraints. Initially, we focused on the concept: effective capacity for cognitive radio channels in order to identify the performance in the presence of QoS constraints. Both underlay and overlay schemes are studied taking into consideration the activity of primary users, and assuming the general case of channel fading as Gamma distribution. For this setting, we further proposed a selection criterion by which the cognitive radio network can choose the adequate mode of operation. Then, we studied the cognitive radio transmissions focusing on Rayleigh fading channel and assumed that no prior channel knowledge is available at the transmitter and the receiver. We investigated the performance of pilot-assisted transmission strategies. In particular, we analyzed the channel estimation using minimum mean-square-error (MMSE) estimation, and analyzed efficient resource allocation strategies. In both cases, power allocations and effective capacity optimization were obtained. Effective capacity and interference constraint were analyzed in both single-band and multi-band spectrum sensing settings. Finally, we studied optimal access probabilities for cognitive radio network using Markov model to achieve maximum throughput for both CR schemes.

Resource allocation for LTE-based cognitive radio network with queue stability and interference constraints

The low power radio access nodes, also known as small cells, are always applied to compensate the coverage of macro cells so as to increase the network capacity. The radio spectrum condition is greatly affected by the buildings and there will be many communication dead zones in the metropolitan area environment if only macro cells are deployed. Generally, the communication dead zone is always small and in irregular shape, therefore, the deployment of small cells is one of the most convincible ways to deal with it. However, the interference, necessary overlapping for handoff, coverage improvement, and cost are correlated to one another. And it is hard to determine the number and locations of small cells to be deployed. In this paper, we propose the Particle Swarm Optimization (PSO) based small cell placement algorithm to effectively deploy the small cells in the communication dead zone of metropolitan area. The fitness value in the PSO process of the proposed scheme is tuned by the adaption function to compromise the coverage maximization and necessary overlapping of small cells. A practical area of Taipei City was adopted as an experimental example to investigate the performance of the proposed scheme. The experimental results illustrate that the proposed scheme places small cells in proper locations so that the coverage can be effectively improved.

A heterogeneous cellular network (HCN) is useful to increase the spectral and energy efficiency of wireless networks and to reduce the traffic load from the macro cell.The performance of the secondary user equipment (SUE) is affected by interference from the eNodeB (eNB) in a macro cell.To decrease the interference between the macro cell and the small cell, allocating resources properly is essential to an HCN.This study considers the scenario of a software-defined heterogeneous cellular network and performs the resource allocation process.First, we show the system model of HCN and formulate the optimization problem.The optimization problem is a complex process including power and frequency resource allocation, which imposes an extremely high complexity to the HCN.Therefore, a hierarchical resource allocation scheme is proposed, which including subchannel selection and a particle swarm optimization (PSO)-based power allocation algorithm.Simulation results show that the proposed hierarchical scheme is effective in improving the system capacity and energy efficiency.

LTE-Advanced (LTE-A) macro-cell deployments are being enhanced with small cells, i.e., low-power base stations, to increase the network coverage and capacity. However, simultaneous co-channel transmissions from macro and small cells cause increased inter-cell interference and under-utilize the spectrum resources at the small cells. The following LTE-A design techniques are used to improve system performance in such deployments: (i) Carrier Aggregation (CA) to increase capacity by using additional carrier bandwidth; (ii) enhanced Inter-Cell Interference Coordination (elCIC), that includes (a) Cell Selection Biasing (CSB) to increase small cell spectrum utilization via cell range expansion; and (b) blanking data transmission on the macro cells for a certain duration of time to increase cell-edge user throughput Our objective is to maximize the CSB of the small cell, subject to user QoS constraints and blanking support from the macro cell. Towards this end, we develop an analytical model that captures the inter-dependency between elCIC techniques. We observe that, not accounting for the complex inter-dependencies between these techniques leads to a degraded network performance. We propose a framework that jointly optimizes elCIC and the assignment of multiple component carriers in an LTE-A deployment for increasing spectrum utilization at the small cells with appropriate blanking support from the macro cells. Our simulation results show that our approach increases the small cell spectrum utilization and aggregate cell-edge throughput by as much as 200%.

We present a general framework for the problem of quality-of-service (QoS) routing with resource allocation for data networks. The framework represents the QoS parameters as functions rather than static metrics. The formulation incorporates the hardware/software implementation and its relation to the allocated resources into a single framework. The proposed formulation allows intelligent adaptation of QoS parameters and allocated resources during a path search, rather than decoupling the path search process from resource allocation. We present a dynamic programming algorithm that, under certain conditions, finds an optimal path between a source and destination node and computes the amount of resources needed at each node so that the end-to-end QoS requirements are satisfied. We present jitter and data droppage analyzes of various rate-based service disciplines and use the dynamic programming algorithm to solve the problem of QoS routing with resource allocation for networks that employ these service disciplines.

Mobile communication is facing new challenges to the soaring traffic demand of numerous user devices; thus, the notion of the small cell has been proposed and realized in recent years. However, licensed spectrum has been occupied by various underlying access technologies, so the deployment of small cells needs a sophisticated planning algorithm. In this article, we provide an overview of reconfigurable radio and small cell technologies, then introduce the tentative network architecture for 5G. Two planning approaches (i.e., genetic-based and graphbased) are proposed that accommodate cognitive radio technology to improve user throughput by eliminating communication interference. Since cognitive radio networking provides frequency allocation with cognition cycle for better spectral efficiency, we tackle the deployment of ultradense small cells and consider the coordination of unlicensed spectrum at the same time. Results show that the proposed algorithms with spectrum cognition improve network performance in terms of throughput and signal-to-interference- plus-noise ratio. Specifically, the genetic- based algorithm increases 232 percent in throughput and 150 percent in signal-to-interference- plus-noise ratio compared to the graphbased algorithm. Finally, we conclude this article by discussing potential challenges and opportunities.

A new flexible multiple access control (MAC) scheme embedding channelization in multi-band carrier sense multiple access / collision avoidance (CSMA/CA) systems is proposed to provide different priority classes among users, and its performance is investigated. In the proposed scheme, two priority classes of users, primary and secondary users, are considered; the classical CSMA/CA protocol is modified to multiband operation for secondary users, whereas channelization is provided for primary users to ensure the quality of service (QoS). The performance of the proposed MAC scheme is analyzed using a new multi-band CSMA/CA model based on a Markov chain capturing the primary user channel activity and the number of bands. The throughput of secondary users is obtained as a function of the primary user activity as well as other CSMA/CA parameters. It is shown that this mixed MAC scheme can guarantee the throughput of primary users certainly with minimal performance degradation to secondary users, especially when the number of available bands is large or the maximum contention window (CW) size for the CSMA/CA secondary users is small compared with the number of secondary users.

This paper studies a cooperative cognitive radio network (CRN), where the primary system leases the licensed band to the secondary system for a fraction of time in exchange for the secondary user (SU) acting as a decode-and-forward relay. Our aim is to determine the cooperative resource allocation strategy among the primary user (PU) and SU so as to minimize both the total power consumption and the leased time. Considering the delay-sensitive applications in CRN, the proposed policy ensures delay-related quality-of-service (QoS) requirements of both PU and SU. To this end, a multi-objective optimization problem of power and time allocation is formulated based on the effective capacity. Via employing weighting method the optimization problem is transformed to a convex one and solved based on the lagrangian dual method. The proposed policy has been shown to be deterministic functions of delay QoS exponent and channel fading states. In particular, the power allocation is achieved by multi-level QoS based water-filling policy. Moreover, numerical results are provided to demonstrate the performance of the proposed resource allocation policy.

Heterogeneous cellular architectures are a promising technology direction for upcoming generations of wireless communication networks. Increasing performance requirements are fulfilled by utilizing a dense deployment of low-power small cells in addition to existing macro cells. In such dense cellular networks it is critical to prevent performance losses from increasing interferences and uneconomic operating costs caused by high power consumptions. These changes in the network architecture create the need for effective control mechanisms specifically designed for heterogeneous networks. Range expansion for small cells has been proposed and extensively researched to achieve load balancing between macro cells and small cells. In this work, we propose a decentralized approach for cell range expansion in small cell networks that in operation only requires very limited local interaction between neighboring cells. We use multiclass support vector machines as a classifier to select suitable parameters for each small cell. Experimental results show that the proposed decentralized approach achieves close to optimal load balancing performance.

We consider how to efficiently employ D2D communications for secondary users (SUs) in a cognitive cellular network. In this network, primary users (PUs) transmit via base station normally, while SUs can employ multiple transmission modes. One is to transmit via base station (BS mode), and the other is to employ D2D communication (D2D mode) due to the scarce idle spectrum. The SUs who have the potential to transmit to each other using D2D mode form a group. Within this group, they can transmit to each other via BS mode or using D2D mode directly. Outside this group, only BS mode is available. To investigate how to employ D2D mode into this network, first we define the utilities of SUs employing BS mode and D2D mode respectively considering achieved data rate, power consumption, price of unit bandwidth and the impact of interference. Then we analyze the optimal power allocation for each mode. To optimize SUs' strategies of mode selection, we adopt replicator dynamics in evolution theory to model the behaviors of SUs. Furthermore, we prove the existence of SUs' evolutionary stable strategy (ESS) of the mode selection process. Based on our model, we finally propose a distributed protocol for SUs within a D2D group to converge to ESS automatically. Numerical results show that our proposed protocol is not only efficient to achieve ESS with improved network performance, but also robust in ESS.

Storaci-Mobile phone communication has become heart of the Global economic development over past decade. As the number of users (human as well as devices) grow exponentially with each year, there is an immense need to increase capacity and throughput with minimum possible upgradation in vast existing network. The 4G-LTE cellular network capacity can be increased by massive deployment of small cells and leveraging very high frequency bands in conjunction with a Macro cell. The presence and usage of small (Phantom) cells are dynamically controlled by a Macro cell. The connection between user equipment (UE) and small cell nodes is achieved by using control and user (C/U) planes splitted at radio link. It can achieve high capacity enhancement by massive deployment of small cells. NS-3 simulations were carried out and results shows capacity enhancement and throughput.