Optimal traffic aggregation in multi-RAT heterogeneous wireless networks

The evolution of cellular networks from one generation to another has led to the deployment of multiple radio access technologies (such as 2G/2.5G/3G/4G) in the same geographical area. This scenario is termed heterogeneous cellular networks. In heterogeneous cellular networks, radio resources can be jointly or independently managed. When radio resources are jointly managed, joint call admission control algorithms are needed for making radio access technology selection decisions. This chapter gives an overview of joint call admission control in heterogeneous cellular networks. It then presents a model of load-based joint call admission control algorithm. Four different scenarios of call admission control in heterogeneous cellular networks are analyzed and compared. Simulations results are given to show the effectiveness of call admission control in the different scenarios. The coexistence of different cellular networks in the same geographical area necessitates joint radio resource management (JRRM) for enhanced QoS provisioning and efficient radio resource utilization. The concept of JRRM arises in order to efficiently manage the common pool of radio resources that are available in each of the existing radio access technologies (RATs) (Perez-Romero et al, 2005). In heterogeneous cellular networks, the radio resource pool consists of resources that are available in a set of cells, typically under the control of a radio network controller or a base station controller. There are a number of motivations for heterogeneous wireless networks. These motivations are (1) limitation of a single radio access technology (RAT), (2) users’ demand for advanced services and complementary features of different RATs, and (3) evolution of wireless technology. Every RAT is limited in one or more of the following: data rate, coverage, security-level, type of services, and quality of service it can provide, etc. (Vidales et al, 2005). A motivation for heterogeneous cellular networks arises from the fact that no single RAT can provide ubiquitous coverage and continuous high QoS levels across multiple smart spaces, e.g. home, office, public smart spaces, etc. Moreover, increasing users’ demand for advanced services that consume a lot of network resources has made network researchers developed more and more spectrally efficient multiple access and modulation schemes to support these services. Consequently, wireless networks have evolved from one generation to another. However, due to huge investment in existing RATs, operators do not readily discard their existing RATs when they acquire new ones. This situation has led to coexistence of multiple RATs in the same geographical area.

Next generation wireless networks will be heterogeneous. Multiple Radio Access Technologies (RATs) will be colocated in the same area. A challenge arising is the efficient radio resource management among overlapped RATs. The concept of Common Radio Resource Management (CRRM) has been proposed in the literature. One of the key issues of CRRM is the RAT selection algorithm. In order to support the conceptual development of Radio Access Technology (RAT) selection algorithms in heterogeneous networks, the theory of Markov model is used. This paper proposes a three-dimensional Markov model for an integrated GERAN/UTRAN/WLAN network based on the extension of existing two co-located RATs Markov models. The performance of two basic RAT selection algorithms: load balancing (LB) based and service based algorithms are evaluated in terms of call blocking probability. The numerical results obtained from our Markov model are validated by simulation results.

The ever increasing user QoS demands and emergence of new user applications make job of network operators and manufacturers more challenging for efficiently optimisation and managing radio resources in radio the radio resources pools of different wireless networks. A group of strategies or mechanisms which are collectively responsible for efficient utilisation of radio resources available within the Radio Access Technologies (RAT) are termed as Radio Resource Management (RRM). The traditional RRM strategies are implemented independently in each RAT, as each RRM strategy considers attributes of a particular access technology. Therefore traditional RRM strategies are not suitable for heterogeneous wireless networks. Common Radio Resource Management (CRRM) or joint radio resource management (JRRM) strategies are proposed for coordinating radio resource management between multiple RATs in an improved manner. In this paper a fuzzy algorithm based CRRM strategy is presented to efficiently utilise the available radio resources in heterogeneous wireless networks. The proposed CRRM strategy balances the load in heterogeneous wireless networks and avoids the unwanted congestion situation. The results such as load distribution, packet drop rate and average throughput at mobile nodes are used to demonstrate the benefits of load balancing in heterogeneous wireless networks using proposed strategy.

Cognitive Radios are considered a standard part of future heterogeneous mobile network architectures. In this chapter, a two tier heterogeneous network with multiple Radio Access Technologies (RATs) is considered, namely (1) the secondary network, which comprises of Cognitive-Femto BS (CFBS), and (2) the macrocell network, which is considered a primary network. By exploiting the cooperation among the CFBS, the multiple CFBS can be considered a single base station with multiple geographically dispersed antennas, which can reduce the interference levels by directing the main beam toward the desired femtocell mobile user. The resultant network is referred to as Smart Cognitive-Femto Network (SCFN). In order to determine the effectiveness of the proposed smart network, the interference rejection capabilities of the SCFN is studied. It has been shown that the smart network offers significant performance improvements in interference suppression and Signal to Interference Ratio (SIR) and may be considered a promising solution to the interference management problems in future heterogeneous networks.

Next generation wireless network (NGWN) will be heterogeneous where different radio access technologies (RATs) coexist. In NGWN, efficient radio resource management and QoS provisioning are major challenges. We propose an optimal joint radio resource management algorithm to minimize call blocking probability in NGWN. The algorithm makes call admission decisions such that overall call blocking probability is minimized in the heterogeneous wireless network. We develop an analytical model for the radio resource management algorithm using a multi-dimensional Markov chain. Based on linear programming optimization technique, we determine the optimal splitting of arriving calls among available RATs. Results show that the proposed algorithm outperforms other algorithm in the heterogeneous wireless network.

The use of multiple radio access technologies (RATs) is inevitable in future heterogeneous cellular networks. Various RATs can offer different throughputs, and thus RAT selection plays an important role in quality of service provisioning. In this paper, considering a heterogeneous network with two throughput classes, we introduce a new practical probabilistic RAT selection approach. In contrary to the common deterministic approaches, in this scheme, each user performs the RAT selection periodically in a random manner using an association probability determined by a central unit in the network. The user remains connected to the selected RAT for a specific amount of time and then again performs the selection. To find the association probabilities, the network throughput maximization is formulated. Since the resulting problem is not convex, we propose two alternatives, one with sub-optimal solution but lower complexity and one with the use of change of variables with optimal solution. Finally, numerical simulations demonstrate the superior performance of our proposed schemes in comparison to several possible approaches. Also, these numerical investigations indicate that our sub-optimal method works very close to the optimal one.

How many small cell (SC) access points (APs) are required to guarantee a chosen quality of service in a heterogeneous network? In this chapter, we answer this question considering two different network models. The first is the downlink of a finite-area SC network where the locations of APs within the chosen area are uniformly distributed. A key step in obtaining the closed-form expressions is to generalize the well-accepted moment matching approximation for the linear combination of lognormal random variables. For the second model, we focus on a two-layer downlink heterogeneous network with frequency reuse-1 hexagonal macro cells (MCs), and SC APs that are placed at locations that do not meet a chosen quality of service from macro base stations (BSs). An important property of this model is that the SC AP locations are coupled with the MC coverage. Here, simple bounds for the average total interference within an MC makes the formulation possible for the percentage of MC area in outage, as well as the required average number of SCs (per MC) to overcome outage, assuming isolated SCs. Introduction Heterogeneous cellular networks (HCNs) are being considered as an efficient way to improve system capacity as well as effectively enhance network coverage [1, 2]. Comprising multiple layers of access points (APs), HCNs encompass a conventional macro cellular network (first layer) overlaid with a diverse set of small cells (SCs) (higher layers). Cell deployment is an important problem in heterogeneous networks, both in terms of the number and positioning of the SCs. Traditional network models are either impractically simple (such as the Wyner model [3]) or excessively complex (e.g., the general case of random user locations in a hexagonal lattice network [4]) to accurately model SC networks. A useful mathematical model that accounts for the randomness in SC locations and irregularity in the cells uses spatial point processes, such as Poisson point process (PPP), to model the location of SCs in the network [5–10]. The independent placement of SCs from the MC layer, has the advantage of analytical tractability and leads to many useful SINR and/or rate expressions. However, even assuming that wireless providers would deploy SCs to support mobile broadband services, the dominant assumption remains that SCs are deployed randomly and independent of the MC layer [11].

It is envisaged that next generation wireless networks (NGWN) will be heterogeneous, consisting of multiple radio access technologies (RATs) coexisting in the same geographical area. In these heterogeneous wireless networks, mobile terminals of different capabilities (heterogeneous terminals) will be used by subscribers to access network services. We investigate the effect of using heterogeneous mobile terminals (e.g. single-mode, dual-mode, triple-mode, etc.) on call blocking and call dropping probabilities in cooperative heterogeneous wireless networks. We develop analytical models for heterogeneous mobile terminals and joint radio resource management in heterogeneous wireless networks. Using a two-class three-RAT heterogeneous wireless network as an example, the effect of using heterogeneous terminals in the network is evaluated. Results show the overall call blocking/dropping probability experienced by subscribers in heterogeneous wireless networks depends on the capabilities of mobile terminals used by the subscribers. In the worst case scenario, when all subscribers use single-mode mobile terminals, each subscriber is confined to a single RAT and consequently, joint radio resource management in heterogeneous wireless network has no improvement on new call blocking and handoff call dropping probabilities. However, in the best case scenario, when all subscribers use three-mode terminals, new class-1 call blocking probability decreases from 0.37 (for 100% single-mode terminals) to 0.05, at the arrival rate of 6 calls per minute. New class-2 call blocking probability also decreases from 0.8 to 0.52. Similarly, handoff class-1 call dropping probability decreases from 0.14 to 0.003, and handoff class-2 call dropping probability decreases from 0.44 to 0.09.

This paper discusses load balancing architecture among heterogeneous wireless networks (HWNs). Load balancing is an important issue for efficient use of radio resources not only in a single network but among different radio access technologies (RATs) in HWNs. Cooperative management of radio resources among heterogeneous RATs is considered to reduce imbalance of utilization of radio resources and enhance availability of the system. For effective load balancing, we define a ldquocommunityrdquo concept, in which radio resources of various RATs are managed together. Radio resource management functional entities of RATs cooperatively work in the community. We present a load balancing architecture applied to 3GPP LTE, WiBro and WLAN and describe architectural considerations. We also present a straightforward load balancing algorithm that takes account of bandwidth usage and received signal to noise ratio. The load balancing algorithm of community radio resource management enhances utilization of radio resources and reduces call blocking probability of individual RATs in the community and further enhances average performance of the whole community.

Considering multiple radio access technologies (RATs) environment for the future wireless access networks, effective RAT discovery and selection are important tasks. They enable discovery of various RATs and let the user select appropriate RAT considering users' preferences and other parameters. The wireless network operator may also take advantage of RAT selection in order to distribute loads among two or more RATs. This paper introduces our pilot design of smart radio research platform (SRRP), on which performance of RAT discovery and selection algorithms are tested. Designing SRRP, we stressed on compliance with existing technical standard specifications, e.g. IEEE P1900.4 and 802.21, efficient RAT discovery, and reasonable RAT selection performance. In regard to RAT discovery algorithm we focused on short discovery time in order to reduce battery consumption and minimize possible quality of service (QoS) degradation. Objective of RAT selection is two folded; one is to choose a satisfactory radio connection and the other one is to maintain the connection reliably. Besides RAT discovery and selection algorithms we incorporated spectrum aggregation capability into SRRP to support user's QoS requirement by allocating an extra spectrum block to a base station (BS) where the existing spectrum block is congested.

Multimode terminals equipped with multiple radio access technologies are becoming increasingly popular. At the same time, network operators and service providers seek opportunities to deliver seamless services cost effectively, leveraging a variety of radio access technologies using both licensed and unlicensed spectrum. In order to standardize the operations of such complex environments 3GPP is currently working on IP flow mobility and mobile data offload solutions. This article proposes and evaluates a new access network selection procedure in such a combined WLAN/LTE environment. The proposed solution takes not only parameters available in the mobile node and its current and candidate access networks into account, but performs an optimization on the heterogeneous wireless network level as well. An optimization model based on an approximate solution to the well-known bin packing problem is presented. Also, there is a signaling scheme for distribution handling presented. Results from simulations performed in OPNET Modeler show improvements compared to basing handover decisions on locally available information only.

Next generation wireless networks (NGWN) will be heterogeneous, comprising a number of radio access technologies (RATs) coexisting in the same geographical area. In NGWN, a joint call admission control (JCAC) algorithm is needed to select the most appropriate RAT for each incoming call. The JCAC algorithm must be user-centric (i.e., consider user preferences in making RAT selection decisions) in order to enhance userspsila satisfaction. This paper investigates a user-centric JCAC (UJCAC) algorithm for heterogeneous wireless networks. The UJCAC algorithm uses Fuzzy multiple-attribute decision-making technique to select the most appropriate RAT for each incoming call. We develop a Markov model for the UJCAC algorithm to evaluate the overall call blocking probability and handoff call dropping probability in heterogeneous wireless networks. Performance of the UJCAC algorithm is evaluated using a three-RAT heterogeneous network. Simulation results show that a UJCAC algorithm can lead to highly unbalanced network load, and high call blocking/dropping probability. Therefore, a flexible UJCAC algorithm is more suitable for NGWN. In flexible UJCAC, if the mostly preferred RAT for incoming call is fully loaded, the next preferred RAT will be selected for the call. The call will be blocked only if none of the available RATs can accommodate the call. Therefore, call blocking/dropping probability will be reduced.

The integration of heterogeneous wireless networks allows the mobile users to benefit simultaneously from the radio coverage of different access technologies (RAT: Radio Access Technology) and the diversity of applications. Nowadays, applications are becoming more and more critical in terms of Quality of Service (QoS) and dynamicity. Handover mechanism is responsible of guaranteeing the required quality of service in ubiquitous environment when mobile users roam across different RATs. The diversification of context metrics offered by these different networks has a direct impact on the user perceived quality of service especially during the network selection process. The present work addresses the network selection process, which is the most important stage of the vertical handover decision mechanism. Our proposed algorithm for Vertical Handover decision making is based on the context awareness and the quality of experience (QoE). It integrates the threshold values of sensitive criteria, which are considered as the required metrics in the making decision process used to select the optimal target network. However, we define a decision function that checks the performance of the given networks and identifies the best solution. Performance offered by the chosen network has to be reviewed since the handover is launched to control the quality of service perceived by the end user. Simulation results improve the perceived network performance by avoiding unnecessary handover. In Ubiquitous environment, most Handover decision mechanisms generate a ping pong effect caused by the inefficiency of the network handover metrics which do not consider the required end user performance. As compared to several existing algorithms, our algorithm fits the expected changes in the network performance and the users preferences without wastage of network resources. It reduces the processing delay and the complexity level caused by unnecessary computation over available radio access technologies which do not ensure required connection performance.

In an increasingly dense and heterogeneous wireless network with various access points, the users are likely to lie in the overlapping coverage areas of multiple radio access technologies, which motivates the boost of user throughput and quality of service via small cell dual-connectivity. In this paper, with the objective of improving network throughput and considering fairness among users, we formulate a small cell selection problem to maximize the network utility reflecting proportional fairness, which is actually the sum of the logarithm of long-term rate of users. However, the formulated problem turns out to be non-convex and combinatorial, and is difficult to be transformed into a convex problem and traditional game-theoretic methods cannot be used. In this context, we introduce a many-to-one matching game with externalities, and develop a distributed algorithm that converges to a stable matching. To further enhance network throughput by mitigating co- channel interference among cellular small cells, a joint cell selection and power control algorithm is developed by adopting an iterative approach to determine them separately and sequentially. Lastly, compared with the nearest distance-based cell selection scheme and the cell selection scheme maximizing the total long-term rate of users, numerical results show the convergence of the matching-based cell selection algorithm and the joint cell selection and power control algorithm, as well as their significant improvement on both total long-term rate of users and user fairness.

There is an increasing demand for high bandwidth-consuming services such as real-time video and video streaming over wireless access networks. A single radio access technology (RAT) in a heterogeneous wireless network may not always have enough radio resource to admit high bandwidth-consuming calls, such as video calls. Existing joint call admission control (JCAC) algorithms designed for heterogeneous wireless networks block/drop an incoming call when none of the available individual RATs in the network has enough bandwidth to admit the incoming call. Consequently, video calls experience high call blocking/dropping probability in the network. However, some calls such as multi-layer coded (scalable) video can be transmitted/received over one or multiple RATs. This article proposes a JCAC algorithm that selects a single or multiple RATs for scalable video calls in heterogeneous wireless networks, depending on availability of radio resources in available RATs. Non scalable calls are always admitted into a single RAT by the algorithm. The aim of the proposed algorithm is to reduce call blocking/dropping probability for both scalable and non-scalable calls. An analytical model is developed for the proposed JCAC algorithm, and its performance is evaluated. Simulation results show that the proposed algorithm reduces call blocking/dropping probability in heterogeneous wireless networks.