An efficient packet parser architecture for software-defined 5G networks

Software-defined networks (SDN) has emerged with the capability to program in order to enhance flexibility, management, and testing of new ideas in the next generation of networks by removing current network limitations. Network virtualization and functionalization are critical elements supporting the delivery of future network services, especially in 5G networks. With the integration of virtualization and functionalization, network resources can be provisioned on-demand, and network service functions can be composed and chained dynamically to cater to various requirements. 5G networks are expected to rely heavily on SDN, which has been widely applied in core network design. To have a software-defined 5G network, not only is new spectrum and interface needed from SDN, but also a programmable and efficient hardware infrastructure is required. Admittedly, hardware components and infrastructure play an important role in supporting 5G networks. In other words, the software-defined 5G network data plane must have the required flexibility and programmability to support upcoming needs and technologies. Technological solutions need to respond to actual requests in infrastructure. Packet parsers in the data plane of software-defined 5G networks are one of the most important components because of the variation in the type of network headers and protocols. Each SDN switch needs to identify headers for processing input packets in the data plane, where the packet parser operates. Multiple implementations of packet parsers have been done on different substrates that occupy large hardware resources and areas on chip. However, they are not suitable for software-defined 5G networks. Certain architectures have been presented for packet parsing, aimed at accelerating the process of header parsing, however no attention has been paid toward reducing the area and the volume of the needed hardware resources and programmability in the data plane. This paper presents a new and efficient architecture for packet parsers on Field Programmable Gate Arrays (FPGA), called Efficient FPGA Packet Parser (EFPP) in a designed software-defined 5G network. This architecture emphasizes the removal of Ternary Content Addressable Memory (TCAM) to decrease hardware resources and efficiency in the data plane. Moreover, this architecture uses the chip’s processing speed and reconfiguration capabilities to support new protocols and network headers while maintaining flexibilities on software-defined 5G networks. EFPP is applied to chips on FPGA Xilinx ZedBoard Zynq, and the resources consumed around 7.5% LookUp Table, 1.9% Flip-Flops, and 5.8% of the memory. EFPP was also more area efficient. According to our results, EFPP would reduce the area and volume of hardware compared to other peer works.     

Rate analysis of ZF receiver for uplink cell-free massive MIMO systems with D2D communications

This paper proposes a transmission structure of zero forcing (ZF) receiver for uplink cell-free massive multiple-input multiple-output (MIMO) systems with device-to-device (D2D) communications, followed by a rate analysis. We assumed that D2D users (DUEs) can utilize orthogonal radio resources to improve the efficiency of the scarce utilization or repurpose the time–frequency-spectrum resources currently used by the cell-free users (CFUEs). Assuming that the imperfect channel state information (CSI) is realizable, after that, the use-and-forget bounding technique is then used to respectively obtain the closed-form expressions of the CFUEs and DUEs, which provide the lower bounds on the ergodic approximate realizable rate of both communication links. First, we calculate the minimum-mean-square error (MMSE) estimation for all channels. Then, the derived results of the achievable uplink sum rate provide us with a tool that enables us to explain how some important parameters, such as the number of access points (APs)/CFUEs, each AP/CFUE/antenna, and the density of DUEs, affect system performance, highlighting the significance of cooperation between cell-free massive MIMO and D2D communication.     

Analysis and simulation of MDMA—A spectrum efficient non-orthogonal multiple access scheme for 5G communication

In this paper, an analysis and simulation approach is used to verify the performance of a recently proposed non-orthogonal multiple access scheme called multipath division multiple access (MDMA). The spatial multipath channel (SMC), which is the distinct feature for each user, is introduced to model the matrix channel of each user, taking into account the multipath structure and massive antennas. The signal and interference powers are then derived. Thus, the corresponding bit error probability can be approximated in a closed form. Moreover, computer simulation results are shown to be consistent with the analysis results. Most importantly, it is revealed that the MDMA system can achieve the superior cellular spectrum efficiency of 25 bps/Hz/cell which fulfills the 5G cellular system requirement.     

Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

The key principle of physical layer security (PLS) is to permit the secure transmission of confidential data using efficient signal-processing techniques. Also, deep learning (DL) has emerged as a viable option to address various security concerns and enhance the performance of conventional PLS techniques in wireless networks. DL is a strong data exploration technique which can be used to learn normal and abnormal behavior of 5G and beyond wireless networks in an insecure channel paradigm. Also, since DL techniques can successfully predict future new instances by learning from existing ones, they can successfully predict new attacks, which frequently involve mutations of earlier attacks. Thus, motivated by the benefits of DL and PLS, this survey provides a comprehensive review that overviews how DL-based PLS techniques can be employed for solving various security concerns in 5G and beyond networks. The survey begins with an overview of physical layer threats and security concerns in 5G and beyond networks. Then, we present a detailed analysis of various DL and deep reinforcement learning (DRL) techniques that are applicable to PLS applications. We present the specific use-cases of PLS design for each type of technique, including attack detection, physical layer authentication (PLA), and other PLS techniques. Then, we present an in-depth overview of the key areas of PLS where DL can be used to enhance the security of wireless networks, such as automatic modulation classification (AMC), secure beamforming, PLA, etc. Performance evaluation metrics for DL-based PLS design are subsequently covered. Finally, we provide insights to the readers about various challenges and future research trends in the design of DL-based PLS for terrestrial communications in 5G and beyond networks.     

Fairness-aware resource allocation for D2D-enabled IoT in NOMA-based cellular networks with mutual successive interference cancellation

Device-to-device (D2D) communications and non-orthogonal multiple access (NOMA) are promising technologies to meet the growing demand for IoT-connected devices. However, they bring about new challenges including the co-channel interference, that can limit the performance improvement. To manage the co-channel interference, we address the problem of joint power allocation and sub-channel assignment for D2D-enabled IoT devices (IoTDs) underlaying a NOMA-based cellular network, in which the successive interference cancellation (SIC) decoding is enabled at the level of IoTDs and cellular user equipment (CUE)to increase the number of connected devices and the capacity. This problem is modeled as a mixed-integer nonconvex optimization problem which includes the concept of fairness with respect to the data rates of IoTDs. To solve the problem, a semi-distributed algorithm is developed, which is of polynomial time complexity. The proposed algorithm leverages the successive convex approximation and a heuristic approach. Evaluation results demonstrate the efficiency of the proposed scheme with respect to the sum rate, fairness, access rate and computational complexity.     

Social-aware relay selection and energy-efficient resource allocation for relay-aided D2D communication

How to improve the flexibility of limited communication resources to meet the increasing requirements of data services has become one of the research hotspots of the modern wireless communication network. In this paper, a novel social-aware motivated relay selection method is put forward to allocate the energy efficiency (EE) resources for the device-to-device (D2D) communication. To improve system flexibility, a D2D user is selected to act as a relay named D2D-relay instead of the traditional cellular relay. The optimal relay selection strategy is formulated by searching the maximum trust value that is obtained by assessing the link stability and social connections between two users. Then, the resource allocation problem, which turns out to be a mixed-integer nonlinear fractional programming (MINLFP) problem, is solved by maximizing the total EE under physical constraint and social constraint synthetically. To improve the solution efficiency, a novel iterative algorithm is proposed by integrating the Dinkelbach theory and Lagrange dual decomposition method. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the existing social-blind and social-aware schemes, it significantly improves the probability of successful relay selection and total EE of the D2D pairs.     

An effective approach for initial access in 5G-millimeter wave-based Vehicle to Everything (V2X) communication using Improved Genetic Algorithm

The process of establishing a directional communication link between the vehicle (VE) on the road and a roadside unit (RSU) is known as initial access process in 5G-millimeter wave(mmWave)-Vehicle to Everything (V2X) communications. Initial access is a tricky problem because substantial interruption or delay can be experienced where the RSU and the VE tries to discover the suitable beam alignment for establishing a direct communication link. Thus, it is very important to resolve this initial access problem in an effective way. Moreover, with the popularity of 5G-mmWave based V2X communication many researchers are trying to address this problem. Therefore, in this paper, we will be presenting a novel beam refinement technique that uses Improved Genetic Algorithm which is quite useful when the system is comprised of large number of antenna arrays. In this work we have considered that RSU and VE are equipped with multiple input multiple output (MIMO) antenna system. Proposed improved genetic algorithm includes some key improvements in terms of selection procedure, elitism, crossover and mutation operations. The effectiveness of the proposed work is investigated in terms of Capacity achieved vs number of transmit and receive antennas at RSU and VE, codebook size, outage probability and total transmitted power. Moreover, a detail analysis has been performed with previous state of the art works in terms of key performance metrics like: capacity achieved by 5G-V2X system, outage probability, and total transmitted power utilization. The proposed work has shown improved beam refinement by solving the initial access problem effectively.