Abdelfatteh Haidine, Abdelhak Aqqal, Hassan Ouahmane

Bio: Abdelfatteh Haidine, Abdelhak Aqqal, Hassan Ouahmane is an academic researcher. The author has contributed to research in topics: Optimization problem & LTE Advanced. The author has an hindex of 1, co-authored 1 publications receiving 18 citations.

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Abstract: Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization.

Millimeter wave beamforming for wireless backhaul and access in small cell networks and practical approaches in software-defined radio

Abstract: Mobile data traffic will continue its tremendous growth in some markets, and has already resulted in an apparent radio spectrum scarcity. There is a strong need for more efficient methods to use spectrum resources, leading to extensive research on increasing spectrum reusability on flexible radio platforms. This study solves this problem in two sub topics, millimeter wave communication on wireless backhaul for spectrum reusability, and flexible prototyping radio platform using software-defined radio (SDR). Wireless backhaul has received significant attention as a key technology affecting the development of future wireless cellular networks because it helps to easily deploy many small size cells, an essential part of a high capacity system. Millimeter wave is considered a possible candidate for cost-effective wireless backhaul. In the outdoor deployment using a millimeter wave, beamforming methods are key techniques to establish wireless links in the 60 GHz to 80 GHz to overcome pathloss constraints (i.e., rainfall effect and oxygen absorption). The millimeter wave communication system cannot directly access the channel knowledge. To overcome this, a beamforming method based on codebook search is considered. The millimeter wave communication cannot access channel knowledge, therefore alternatively a beamforming method based on a codebook search is considered. In the first part, we propose an efficient beam alignment technique using adaptive subspace sampling and hierarchical beam codebooks. A wind sway analysis is presented to establish a notion of beam coherence time. This highlights a previously unexplored tradeoff between array size and wind-induced movement. Generally, it is not possible to use larger arrays without risking a performance loss from wind-induced beam misalignment. The performance of the proposed alignment technique is analyzed and compared with other search and alignment methods. Results show significant performance improvement with reduced search time. In the second part of this study, SDR is discussed as an approach toward flexible wireless communication systems. Most layers of SDR are implemented by software. Therefore, only a software change is needed to transform the type of radio system. The translation of the signal processing into software performed by a regular computer opens up a huge number of possibilities at a reasonable price and effort. SDR systems are widely used to build prototypes, saving time and money. In this project, a robust wireless communication system in high interference environment was developed. For the physical layer (PHY) of the system, we implemented a channel sub-bandding method that utilizes frequency division multiplexing to avoid interference. Then, to overcome a further interfered channel, Direct Spread Spectrum System (DSSS) was considered and implemented. These prototyped testbeds were evaluated for system performance in the interference environment.

Resource Allocation with Flexible Channel Cooperation in Cognitive Radio Networks

Abstract: We study the resource allocation problem in an OFDMA based cooperative cognitive radio network, where secondary users relay data for primary users in order to gain access to the spectrum. In light of user and channel diversity, we first propose FLEC, a novel flexible channel cooperation scheme. It allows secondary users to freely optimize the use of channels for transmitting primary data along with their own, in order to maximize performance. Further, we formulate a unifying optimization framework based on Nash bargaining solutions to fairly and efficiently allocate resources between primary and secondary networks, in both decentralized and centralized settings. We present an optimal distributed algorithm and a sub-optimal centralized heuristic, and verify their effectiveness via realistic simulations. Under the same framework, we also study conventional identical channel cooperation as the performance benchmark, and propose algorithms to solve the corresponding optimization problems. KEYWORDS—Cognitive radio, Cooperative Communication, Resource Allocation, Nash bargaining solutions, OFDMA.

5G Signal Identification Using Deep Learning

Abstract: Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.