Soheil Rostami

Order-Statistic Based Spectrum Sensing for Cognitive Radio

Spectrum sensing for cognitive radio is challenging. In this letter, a spectrum sensing method based on quintiles of Order-Statistics is proposed. We derive the test statistic and evaluate the performance of the proposed method by Monte Carlo simulations. Simulation results show that order statistics based sensing considerably outperforms both energy detection and anderson darling based sensing in an Additive White Gaussian Noise (AWGN) channel; especially in a lower signal to noise ratio region.

A joint resource allocation and link adaptation algorithm with carrier aggregation for 5G LTE-Advanced network

Carrier aggregation has been introduced by 3rd Generation Partnership project (3GPP) in order to meet the IMT-Advanced requirements. Using carrier aggregation, multiple component carriers with a different bandwidth, dispersed within intra or inter bands can be simultaneously utilised to provide higher data rates, better coverage and lower latency resulting a better user experience. However, carrier aggregation functionality requires modification to the radio resource management function of the network. In this paper, we propose an efficient resource allocation and link adaptation algorithm to support carrier aggregation functionality for downlink 5G LTE-Advanced (LTE-A) network. We consider a non-guaranteed bit rate bearers (best effort traffic), which can be used for non realtime applications such as file downloads. Most of the existing work considered resource allocation, component carrier selection and link adaptation as separate problems. We define a joint radio resource management problem with carrier aggregation functionality and propose a sub-optimal solution with a low computational complexity. Simulation results show clearly that our algorithm outperforms state-of-the-art algorithms.

Energy-Efficient Resource Allocation for LTE-A Networks

Energy-efficient resource allocation can significantly reduce power consumption of network and, consequently, operating expenses. In this letter, we study the energy efficiency of long-term evolution (LTE) networks with carrier aggregation, where user equipments are able to utilize multiple component carriers simultaneously. Furthermore, an LTE-compliant optimal resource block allocation algorithm to maximize throughput while minimizing energy consumption is proposed. Single-cell multi-user simulation results show that the proposed algorithm offers significant gains in terms of cell throughput and energy consumption over the state-of-the-art solutions.

Energy Efficient Cognitive M2M Communications

The number of Machine-to-Machine M2M devices has increased massively in the last few years and will continue to increase in the years to come. Spectrum utilisation efficiency and energy efficiency are the main challenges and design goals for M2M networks. Cognitive radio CR is a promising technology that can address these challenges. In this paper, the authors have proposed and developed an energy efficient mechanism to reduce energy consumption in Cognitive M2M CM2M networks. Their solution guarantees the throughput and reliability constraints for CM2M Devices CM2MDs. The proposed mechanism can reduce energy consumption in CM2M networks by exploiting efficient sensing and accessing schemes for CM2MDs. The authors further develop sleep-mode/switching and accessing techniques to work efficiently with the proposed mechanism. The simulation results show that the proposed mechanism guarantees a desirable throughput and reduces overall energy consumption in the network.

Resource allocation algorithms for OFDM based wireless systems

Carrier Aggregation (CA) is a key functionality for next generation wireless communication systems that enables operators to create larger virtual carrier bandwidths for data hungry applications by aggregating available Component Carriers (CCs). In this paper, the Resource Allocation (RA) optimisation problem in Orthogonal Frequency Division Multiplexing (OFDM) based wireless systems is studied with CA. We show that RA with CA can be formulated as a complex mixed integer programming optimisation problem under the practical constraints of transmit power and limited number of CCs available for each user. A two-step solution is proposed, Step 1: CC allocation is performed to satisfy users CA constraints and remove the intractable integer constraints of the optimisation problem, Step 2: power and SubCarrier (SC) allocation algorithm assigns resources among users sub-optimally. We propose two efficient methods based on our two-step solution with reasonably low complexity refer as Two-Step RA Algorithm (TSRAA) and Efficient RA Algorithm (ERAA). The performance of TSRAA and ERAA are compared with the highly complex Combinatorial Algorithm (COA) introduced as an optimal solution. Simulation results verify that TSRAA reaches to optimum solution and exploits the multi-CC diversity of wireless systems. Furthermore, numerical results show that ERAA achieves satisfying performance with very low complexity.

Resource Allocation in LTE-Based MIMO Systems with Carrier Aggregation

Resource Allocation (RA) is one of the crucial elements in Long Term Evolution (LTE) networks, which has a tremendous impact on the network performance. However, complexity of the RA problem increases when both Carrier Aggregation (CA) and Multiple-Input Multiple-Output (MIMO) are enabled in the network. CA is a key functionality for LTE- Advanced (LTE-A) that enables Mobile Networ Operators (MNOs) to create large virtual carrier bandwidths for data hungry applications by aggregating available Component Carriers (CCs). In this paper, Resource Block (RB), Secondary CC (SCC), MIMO Transmission Mode (TM), and Modulation and Coding Scheme (MCS) selection is formulated jointly as an Integer Linear Programming (ILP). We prove that intractable integrality of the optimisation problem can be removed and relaxed to the Linear Programming (LP). Solving the LP obtains an optimal solution for the original ILP.

Spectrum Assignment Algorithm for Cognitive Machine-to-Machine Networks

A novel aggregation-based spectrum assignment algorithm for Cognitive Machine-To-Machine (CM2M) networks is proposed. The introduced algorithm takes practical constraints including interference to the Licensed Users (LUs), co-channel interference (CCI) among CM2M devices, and Maximum Aggregation Span (MAS) into consideration. Simulation results show clearly that the proposed algorithm outperforms State-Of-The-Art (SOTA) algorithms in terms of spectrum utilisation and network capacity. Furthermore, the convergence analysis of the proposed algorithm verifies its high convergence rate.