Seiamak Vahid

A Survey of Radio Resource Management for Spectrum Aggregation in LTE-Advanced

In order to satisfy the requirements of future IMT-Advanced mobile systems, the concept of spectrum aggregation is introduced by 3GPP in its new LTE-Advanced (LTE Rel. 10) standards. While spectrum aggregation allows aggregation of carrier components (CCs) dispersed within and across different bands (intra/inter-band) as well as combination of CCs having different bandwidths, spectrum aggregation is expected to provide a powerful boost to the user throughput in LTE-Advanced (LTE-A). However, introduction of spectrum aggregation or carrier aggregation (CA) as referred to in LTE Rel. 10, has required some changes from the baseline LTE Rel. 8 although each CC in LTE-A remains backward compatible with LTE Rel. 8. This article provides a review of spectrum aggregation techniques, followed by requirements on radio resource management (RRM) functionality in support of CA. On-going research on the different RRM aspects and algorithms to support CA in LTE-Advanced are surveyed. Technical challenges for future research on aggregation in LTE-Advanced systems are also outlined.

Licensed Spectrum Sharing Schemes for Mobile Operators: A Survey and Outlook

The ongoing development of mobile communication networks to support a wide range of superfast broadband services has led to massive capacity demand. This problem is expected to be a significant concern during the deployment of the 5G wireless networks. The demand for additional spectrum to accommodate mobile services supporting higher data rates and having lower latency requirements, as well as the need to provide ubiquitous connectivity with the advent of the Internet of Things sector, is likely to considerably exceed the supply, based on the current policy of exclusive spectrum allocation to mobile cellular systems. Hence, the imminent spectrum shortage has introduced a new impetus to identify practical solutions to make the most efficient use of scarce licensed bands in a shared manner. Recently, the concept of dynamic spectrum sharing has received considerable attention from regulatory bodies and governments globally, as it could potentially open new opportunities for mobile operators to exploit spectrum bands whenever they are underutilized by their owners, subject to service level agreements. Although various sharing paradigms have been proposed and discussed, the impact and performance gains of different schemes can be scenario-specific, and may vary depending on the nature of the sharing players, the level of sharing and spectrum access scheme. In this survey, we study the main concepts of dynamic spectrum sharing, different sharing scenarios, as well as the major challenges associated with sharing of licensed bands. Finally, we conclude this survey with open research challenges and suggest some future research directions.

A survey on clustering techniques for cooperative wireless networks

Clustering became relevant in the past as a solution for the scalability problems of ad hoc networking, but, the unsuccessful application of ad hoc solutions to real scenarios, such as the projects SURAN and PRNet, decreased the interest of research community on ad hoc communications, and subsequently, on clustering algorithms. Recently, however, clustering techniques have gained renewed interest due to the emergence of cooperative communications for cellular networking. Clustering is envisaged, in this scenario, as a technique to team up nodes to support efficient data aggregation for energy saving, scalability and privacy among other benefits. Moreover, research on 5G networks also envisages a connected society, where everything and everyone will be connected under the umbrella of Internet of Everything (IoE). This novel communication paradigm has fostered new research on clustering, which has yielded novel and more advanced algorithms and applications. This article surveys the State-of-the-Art in clustering techniques and provides detailed descriptions of the basics of clustering and the latest novel ideas. Open issues, technical challenges and directions for future research are also outlined.

Dynamic Heterogeneous Learning Games for Opportunistic Access in LTE-Based Macro/Femtocell Deployments

Interference is one of the most limiting factors when trying to achieve high spectral efficiency in the deployment of heterogeneous networks (HNs). In this paper, the HN is modeled as a layer of closed-access LTE femtocells (FCs) overlaid upon an LTE radio access network. Within the context of dynamic learning games, this work proposes a novel heterogeneous multiobjective fully distributed strategy based on a reinforcement learning (RL) model (CODIPAS-HRL) for FC self-configuration/optimization. The self-organization capability enables the FCs to autonomously and opportunistically sense the radio environment using different learning strategies and tune their parameters accordingly, in order to operate under restrictions of avoiding interference to both network tiers and satisfy certain quality-of-service requirements. The proposed model reduces the learning cost associated with each learning strategy. We also study the convergence behavior under different learning rates and derive a new accuracy metric in order to provide comparisons between the different learning strategies. The simulation results show the convergence of the learning model to a solution concept based on satisfaction equilibrium, under the uncertainty of the HN environment. We show that intra/inter-tier interference can be significantly reduced, thus resulting in higher cell throughputs.

Machine Learning Based Knowledge Acquisition on Spectrum Usage for LTE Femtocells

The decentralised and ad hoc nature of femtocell deployments calls for distributed learning strategies to mitigate interference. We propose a distributed spectrum awareness scheme for femtocell networks, based on combined payoff and strategy reinforcement learning (RL) models. We present two different learning strategies, based on modifications to the Bush Mosteller (BM) RL and the Roth-Erev RL algorithms. The simulation results show the convergence behaviour of the learning strategies under a dynamic robust game. As compared to the Bush Mosteller (BM) RL, our modified BM (MBM) converges smoothly to a stable satisfactory solution. Moreover, the MBM significantly reduces the interference collision cost during the learning process. Both the MBM and the modified Roth-Erev (MRE) algorithms are stochastic-based learning strategies which require less computation than the gradient follower (GF) learning strategy and have the capability to escape from suboptimal solution.

Lightweight security against combined IE and SSDF attacks in cooperative spectrum sensing for cognitive radio networks

Cognitive radio is envisaged as a promising solution to cope with the problem of spectrum scarcity. In cognitive radio networks, users can sense the medium and opportunistically use available frequency bands. Users can cooperate in order to increase the reliability of the sensing process, which is called cooperative spectrum sensing CSS. However, cooperative paradigms are threatened by the behavior of malicious users. Two types of attacks represent the main threats for CSS network operation, namely, spectrum sensing data falsification SSDF and incumbent emulation IE. These two types of attacks have received a considerable amount of attention in the literature, but they have always been studied separately. In this paper, we propose a novel mechanism based on lightweight cryptography that considers both SSDF and IE attacks combined in a CSS network for the first time. Lightweight cryptography, in contrast to previous techniques used such as intrusion detection or reputation systems, provides higher resilience to such attacks when a high number of mobile malicious users exist, providing better energy efficiency. Analytical and simulation results show the outperformance of the proposed algorithm compared with previous mechanisms, in terms of lower false alarm probability i.e., higher chance of using the free frequency bands and hence better energy ratings. Copyright

A cognitive approach for stable cooperative group formation in mobile environments

Cooperative communications have been gaining huge attention lately, due to the increase in the number of mobile devices and the advancement in their capabilities. In this framework, a commonly suggested approach to benefit from cooperation is the formation of virtual groups of mobile terminals. Mobility-aware algorithms are commonly proposed, where nodes form cooperative clusters according their basic mobile characteristics. However, more detailed mobility pattern information is not used to improve the performance of these systems. The reason is the complexity of current mobility pattern recognition algorithms that require previous set up and advanced knowledge on the scenario. This paper presents a novel cognitive algorithm for the identification of mobility patterns that can be used for mobile group formation and that requires no configuration. The proposed algorithm, based on data mining techniques and cooperative optimization, can evaluate the number of mobility patterns in the scenario and classify nodes according these patterns in a distributive fashion. The algorithm is mathematically presented and tested with extended simulations in both Matlab and ns-2.

SINR Based Topology Control for Multihop Wireless Networks with Fault Tolerance

In this paper, an optimal centralized approach to topology control (TC) is adopted where the network topology is established considering interference, k-connectivity and routing constraints. This optimization problem however involves link scheduling and power assignment under SINR constraint, which is an NP hard problem even for a small number of nodes. An exact solution beyond six nodes has not been found so far. Opting for heuristics rather than exact approach, the proposed algorithms in the literature, either cannot guarantee the quality of the solution, or approximate the interference (protocol interference model) rather than using realistic SINR models. Here, at first we present a novel formulation for the optimal solution and analyse its limits. We then propose a novel approximation algorithm using column generation (CG) together with knapsack transformation on the SINR constraint. Particle Swarm Optimization (PSO) is integrated into the CG, to provide robust initial feasible patterns. The results show that, CG-PSO with knapsack transformation increase the solvable instances three fold in terms of number of nodes, in comparison to the state-of-art approaches. The links are scheduled with less power and shorter scheduling lengths,while the proposed algorithm also reduces the computation time at lower penalty cost.

The study on spectrum/channel fragmentation from dynamic spectrum aggregation in CRNs

We study the dynamic spectrum aggregation and allocation problem in cognitive radio networks (CRNs). When the available spectrum is fragmented and the bandwidth of the spectrum holes is narrow, spectrum aggregation can be a solution to use multiple holes. Since the spectrum holes can have different bandwidth and cognitive radios can dynamically adjust the centre frequency and the bandwidth for each transmission, the CRN has the high flexibility in spectrum use with aggregation. However, aggregation of fragmented spectrum can lead to channel fragmentation and further spectrum fragmentation. In this context, we formulate the dynamic spectrum aggregation problem considering practical hardware constraints whilst aiming to minimise spectrum/channel fragmentation. Then, we study the application of three well-known memory allocation techniques, to spectrum aggregation, and analyse the impacts of the spectrum aggregation on spectrum/channel fragmentation. Based on the analysis, we derive guidelines that govern the design of the efficient spectrum aggregation and allocation algorithms.

Practical Spectrum Aggregation for Secondary Networks With Imperfect Sensing

We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address the following fundamental query: How much spectrum aggregation is worthy with imperfect sensing? For collision occurrence, we focus on two different types of collision: One is imposed by asynchronous transmission, and the other is imposed by imperfect spectrum sensing. The collision probability expression has been derived in closed form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and number of aggregated subchannels. In addition, the impact of spectrum aggregation on data rate is analyzed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results clearly show that the proposed approach outperforms the benchmark that passively aggregates subchannels with lack of collision awareness.