Long Term Evolution (LTE) is defined by the Third Generation Partnership Project (3GPP) standards as Release 8/9. The LTE supports at max 20 MHz channel bandwidth for a carrier. The number of LTE users and their applications are increasing, which increases the demand on the system BW. A new feature of the LTE-Advanced (LTE-A) which is defined in the 3GPP standards as Release 10/11 is called Carrier Aggregation (CA), this feature allows the network to aggregate more carriers in-order to provide a higher bandwidth. Carrier Aggregation has three main cases: Intra-band contiguous, Intra-band non-contiguous, Inter-band contiguous. The main contribution of this paper was in implementing the Intra-band contiguous case by modifying the LTE-Sim-5, then evaluating the Quality of Service (QoS) performance of the Modified Largest Weighted Delay First (MLWDF), the Exponential Rule (Exp-Rule), and the Logarithmic Rule (Log-Rule) scheduling algorithms over LTE/LTE-A in the Down-Link direction. The QoS performance evaluation is based on the system's average throughput, Packet Loss Rate (PLR), average packet delay, and fairness among users. Simulation results show that the use of CA improved the system's average throughput, and almost doubled the system's maximum throughput. It reduced the PLR values almost by a half. It also reduced the average packet delay by 20-40\% that varied according to the video bit-rate and the number of users. The fairness indicator was improved with the use of CA by a factor of 10-20%.

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Long Term Evolution (LTE) is defined by the Third Generation Partnership Project (3GPP) standards as KEYWORDS

This research paper aims at comparing two multi-core processors machines, the Intel core i7-4960X processor (Ivy Bridge E)

This research paper aims at comparing two multi-core processors machines, the Intel core i7-4960X processor (Ivy Bridge E) and the AMD Phenom II X6. It starts by introducing a single-core processor machine to motivate the need for multi-core processors. Then, it explains the multi-core processor machine and the issues that rises in implementing them. It also provides a real life example machines such as TILEPro64 and Epiphany-IV 64-core 28nm Microprocessor (E64G401). The methodology that was used in comparing the Intel core i7 and AMD phenom II processors starts by explaining how processors' performance are measured, then by listing the most important and relevant technical specification to the comparison. After that, running the comparison by using different metrics such as power, the use of Hyper-Threading technology, the operating frequency, the use of AES encryption and decryption, and the different characteristics of cache memory such as the size, classification, and its memory controller. Finally, reaching to a roughly decision about which one of them has a better over all performance.

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning (RL), more specifically, the Q-learning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users (PUs), and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users (SUs), and they could be un-licensed subscribers that don't pay for their service, device-to-device communications, or sensors. Each user whether it’s a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost 90% utilization of the spectrum, and provided fair shares of the spectrum among users.

This survey paper provides a detailed explanation of Long Term Evolution (LTE) cellular network’s packet scheduling algorithms in both downlink and uplink directions. It starts by explaining the difference between Orthogonal Frequency Division Multiple Access (OFDMA) that is used in downlink transmission, and Single Carrier – Frequency Division Multiple Access (SC-FDMA) is used in uplink. Then, it explains the difference between the LTE scheduling process in the donwlink and uplink through explaining the interaction between users and the scheduler. Then, it explains the most commonly used downlink and uplink scheduling algorithms through analyzing their formulas, characteristics, most suitable conditions for them to work in, and the main differences among them. This explanation covers the Max Carrier-toInterference (C/I), Round Robin (RR), Proportional Fair (PF), Earliest Deadline First (EDF), Modified EDF-PF, Modified-Largest Weighted Delay First (M-LWDF), Exponential Proportional Fairness (EXPPF), Token Queues Mechanism, Packet Loss Ratio (PLR), Quality Guaranteed (QG), Opportunistic Packet Loss Fair (OPLF), Low Complexity (LC), LC-Delay, PF-Delay, Maximum Throughput (MT), First Maximum Expansion (FME), and Adaptive Resource Allocation Based Packet Scheduling (ARABPS). Lastly, it provides some concluding remarks.