Data Rate Requirements Of Users Research Articles

Deep Learning Based Power Allocation Schemes for NOMA System with Imperfect SIC

Non-orthogonal multiple access (NOMA) has been a promising technology for 5G communication system. NOMA allows more than one user to utilize the same resource at the same time, which can lead to high performance in terms of spectral efficiency, energy efficiency or fairness. NOMA enables power domain multiplexing and separates the multiplexed signal by using successive interference cancellation (SIC). Therefore, the full benefit of NOMA depends on power allocation. However, in practical system, residual interference caused by SIC process can severely degrade a system performance. In this paper, we propose two power allocation schemes based on deep learning to alleviate the effect of imperfect SIC for downlink NOMA system, including a deep learning-based sum rate power allocation scheme (DL-SRPAS) and a deep learning-based energy-efficient power allocation scheme (DL-EEPAS). our two proposed schemes learn two optimal power allocation schemes provided through exhaustive search. We search the solution of the optimization problems, where two optimization problems are formulated to maximize the sum rate and the energy efficiency subject to a minimum user data rate requirement. The simulation results verify that our two proposed schemes can alleviate the effect of imperfect SIC and outperform two conventional power allocation schemes which maximize the sum rate and the energy efficiency without considering imperfect SIC. In addition, our proposed schemes achieve near-optimal performance with very low computational time.

Efficient resource allocation for hybrid nonorthogonal multiple access based heterogeneous networks beyond fifth‐generation

AbstractFifth‐generation (5G) and future beyond fifth‐generation (B5G) networks will require immense capacity due to the high rise in the number of multimedia applications and mobile devices. The heterogeneous networks (HetNets) in 5G and B5G can increase the heterogeneity and network throughput because macro base station‐only (MBS‐only) networks cannot satisfy the drastic increase in capacity demands. Hybrid nonorthogonal multiple access (H‐NOMA) can accommodate the increasing number of multimedia applications and mobile devices in 5G and B5G. H‐NOMA in B5G can also improve spectral efficiency. Before this, the researchers had not considered user clustering with H‐NOMA in HetNets. This article investigates the user clustering with downlink H‐NOMA in HetNet to maximize the network throughput in 5G and B5G. The formulated mathematical problem optimizes the considered key performance indicators (KPIs), that is, users admission in clusters, users association with base stations, power allocation to users, and network throughput. At the same time, it also satisfies the minimum transmit power and data rate requirements of users. The formulated problem is a mixed‐integer nonlinear programming (MINLP) problem. We have proposed an‐optimal algorithm, that is, outer approximation algorithm (OAA) to solve the MINLP problem because the complexity of the optimal exhaustive search algorithm (ESA) increases exponentially with an increase in the number of users. H‐NOMA with user clustering in HetNet is evaluated with extensive simulations to show its effectiveness regarding network throughput in 5G and B5G. We have also considered the proposed framework in the MBS‐only network and compared its performance with HetNet. The results verify that the proposed framework in HetNet performs better than the MBS‐only network. The complexity of the‐optimal algorithm is calculated that gives‐optimal results within. We have also made a complexity analysis of the proposed OAA and ESA and concluded that the complexity of the proposed OAA algorithm is less than the ESA.

Enhancing energy efficiency for cellular-assisted vehicular networks by online learning-based mmWave beam selection

Millimeter Wave (mmWave) technology has been regarded as a feasible approach for future vehicular communications. Nevertheless, high path loss and penetration loss raise severe questions on mmWave communications. These problems can be mitigated by directional communication, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed the beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this kind of work focuses on network throughput rather than network energy efficiency, which ignores the consideration of energy consumption. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall. In EFML, the energy consumption is reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users, and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results demonstrate that, compare with the comparison method with best energy efficiency, the proposed EFML improves energy efficiency by 17–41% in different scenarios.

Online Green Communication Scheduling for Sliced Unlicensed Heterogeneous Networks

New radio in unlicensed spectrum (NR-U) and network slicing have been emerged as two promising technologies to meet with the demand of enormous traffic volume, extreme transmission speed, and highly heterogeneous service requirements in future 5 G networks. Additionally, spurred by both economic considerations and environmental concerns, green communication becomes a new prominent figure of merit in the design of future 5 G networks. In this paper, we investigate an energy consumption scheduling for unlicensed heterogeneous networks, namely NR-U and WiFi, by leveraging the network slicing technology. First, we introduce a network slice broker, which enables virtual heterogeneous network operators to request and lease resources cooperatively via signaling means. Then, we investigate a joint network selection and resource allocation scheduling to minimize the network energy consumption in real time environment, taking into account various user data rate and delay requirements. An online Lyapunov optimization algorithm has been introduced to solve the proposed problem with low computational complexity and rapid convergence. We show that there exists a tradeoff between energy consumption and traffic delay, which can be adjusted through control parameters. Numerical results are presented to confirm our analyses and demonstrate the effectiveness of the proposed algorithms.

A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network

Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area.

Joint Uplink and Downlink Resource Allocation for NOMA-Enabled D2D Communications

This letter investigates the joint uplink and downlink resource allocation problem, where the non-orthogonal multiple access (NOMA) technology is applied to enhance device-to-device (D2D) communications. In order to maximize the sum data rate of NOMA-enabled D2D groups while guaranteeing the minimum data rate requirements of both cellular users (CUs) and D2D groups, we first design a subcarrier assignment algorithm to assign the downlink and uplink subcarriers for D2D groups, and then transform the problem into a convex optimization to obtain the optimal power allocation. Simulation results demonstrate the remarkable improvement in terms of data rate of D2D groups by using our algorithms.

QoS-Compliant 3-D Deployment Optimization Strategy for UAV Base Stations

Unmanned aerial vehicle (UAV) is being integrated as an active element in 5G and beyond networks. Because of its flexibility and mobility, UAV base stations (UAV-BSs) can be deployed according to the ground user distributions and their quality of service (QoS) requirement. Although there has been quite some prior research on the UAV deployment, no work has studied this problem in a 3 dimensional (3D) setting and taken into account the UAV-BS capacity limit and the quality of service (QoS) requirements of ground users. Therefore, in this paper, we focus on the problem of deploying UAV-BSs to provide satisfactory wireless communication services, with the aim to maximize the total number of covered user equipment (UE) subject to user data rate requirements and UAV-BSs' capacity limit. First, we model the relationship between the air-to-ground (A2G) path loss (PL) and the location of UAV-BSs in both horizontal and vertical dimensions which has not been considered in previous works. Unlike the conventional UAV deployment problem formulation, the 3D deployment problem is decoupled into a 2D horizontal placement and altitude determination connected by path loss requirement and minimization. Then, we propose a novel genetic algorithm (GA) based 2D placement approach in which UAV-BSs are placed to have maximum coverage of the users with consideration of data rate distribution. Finally, numerical and simulation results show that the proposed approach has enabled a better coverage percentage comparing with other schemes.

Energy Efficient Resource Allocation for Underlaying Multi-D2D Enabled Multiple-Antennas Communications

Energy efficiency has a significant importance to optimize the wireless communications systems by providing high data rates. In order to develop energy efficient systems, one of the promising methods is to use multiple device-to-device (D2D) underlaying multiple antenna cellular systems. The interference from cellular users to D2D pairs, the interference between D2D pairs and the interference at the base station (BS) caused by D2D pairs occur in these communications systems. In this article, we propose energy efficient resource allocation algorithms for underlaying multi-D2D enabled multiple-antennas communications by employing different multiple antenna processing techniques at the BS. A joint method based on Dinkelbach algorithm and Message Passing Algorithm (MPA) and an approach based on deep learning with multi-layer artificial neural network are proposed to maximize the global energy efficiency (GEE) while satisfying the data rate requirements of both cellular users and D2D pairs. In MPA, the factor graph of the D2D pairs is constructed by taking into account the interference among the D2D pairs and the interference level at the BS to avoid any interruption in the cellular transmission. By relying on the training based on the proposed joint algorithm, a deep neural network approach is presented for off-line implementation. The performance results of the proposed energy efficient resource allocation algorithms show the superiority of multi-D2D communications over conventional single-D2D communications.

Antenna Selection and Designing for THz Applications: Suitability and Performance Evaluation: A Survey

Imperceptible latency, uninterrupted communication, and the availability of inexhaustible bandwidth are conceptualized as essential milestones to revolutionize the modes by which societies generated, circulate, receive, and perceive information. The exponential increase in wireless data traffic has raised concerns to investigate suitable bands in the radio spectrum to satisfy the intensifying user's data rate requirements. Overall the wireless infrastructure needs development and exploitation to synchronize with the massive capacity and connectivity demands. The Terahertz (THz) frequency band (0.1-10 THz) is considered as a pivotal solution to fulfill the needs of applications and devices requiring the high speed transmission, and have received noticeable attention from the research community. Technologies in this spectrum are facing rapid development and hold high potentials in applications like ultra-fast short-range wireless communications, remote sensing, biological detection, and basic material research. The antenna is one of the critical components to support the THz systems and require a considerable attention in terms of precision. Compact high-gain antennas are desirable for low latency and high data rate THz wireless communication systems, specifically for applications having space limitation, for example, in the high speed interlink inside the high density wireless communication base station (BS). Nevertheless, there still exist many challenges, while designing the antenna for THz communications requiring innovative solutions. This paper serves an introductory guideline to address the challenges and opportunities, while designing a THz enabled antenna.

Trigonometric transforms and precoding strategies for OFDM‐based uplink hybrid multi‐carrier nonorthogonal multiple access

AbstractAn uplink hybrid nonorthogonal multiple access (h‐NOMA) scheme utilizing power domain multiplexing is adopted in this paper for orthogonal frequency–division multiplexing (OFDM)–based systems. The OFDM‐based NOMA systems can achieve high spectral efficiency with resilience to wireless multipath fading. The h‐NOMA maintains a low interference level per resource block or low complexity of multiuser detection (MUD) implementation at the receiver and also achieves consistent fairness performance by restricting the maximum number of users per resource block. An improved user clustering algorithm for OFDM‐based h‐NOMA system under the assumption of user's distinct data rate requirement is presented here. The proposed algorithm takes into consideration both the channel gain of all activated users and the different data rate requirements of weak channel gain users. High peak‐to‐average power ratio (PAPR) and performance degradation due to strict frequency synchronization requirements are the main drawbacks of such an OFDM‐based uplink h‐NOMA system. High PAPR of multicarrier NOMA is addressed here with precoding techniques. An enhanced minimum mean square error (MMSE) receiver performing joint equalization and carrier frequency offset (CFO) compensation using low‐complexity banded‐matrix implementation is proposed to cancel interference in the frequency domain. The MUD is implemented with low‐complexity successive interference cancellation (SIC) using bandedmatrix approximation of the interference matrix in the presence of CFO. Moreover, the possibility of utilizing efficient trigonometric transforms to implement OFDM‐based NOMA is studied with CFO and estimation errors. For OFDM‐based h‐NOMA system, an efficient combination of different trigonometric transform configurations to constitute the multicarrier basis for OFDM with a suitable precoding strategy for PAPR reduction is studied.

Transmit Power Minimization for Downlink Multi-Cell Multi-Carrier NOMA Networks

In this letter, to minimize the total transmit power with constraints of the user data rate requirements, the radio resource allocation problem for multi-cell and multi-carrier non-orthogonal multiple access (MCMC-NOMA) network, which proves to be an NP-hard problem, is investigated. Therefore, to deal with this optimization problem, a centralized minimum power control algorithm with the fixed user assignment is developed first, upon which a greedy user clustering and power allocation scheme is proposed. The Monte Carlo simulation results demonstrate that the proposed resource allocation algorithm can dramatically reduce the total power consumption compared with ones in the existing literatures. Furthermore, NOMA really outperforms OMA networks in terms of transmit power consumption.

Hybrid Precoding With Rate and Coverage Constraints for Wideband Massive MIMO Systems

Hybrid architecture is recognized as complexity and cost effective for the implementation of massive multi-input multi-output (MIMO) systems, where analog precoder is used to form narrow beams toward users for data transmission with reduced number of radio frequency chains. However, besides transmitting the user-specific data, the system also needs to broadcast control signaling intended for all users simultaneously, which requires wide beams to ensure the coverage. In this paper, we study hybrid precoder design for downlink space-division multiaccess and orthogonal frequency-division multiplexing wideband massive MIMO systems, aimed at minimizing the total transmit power of the base station, subject to both the coverage constraint of signaling and data rate requirements of users. We first derive the coverage probability of massive MIMO systems with hybrid architecture under general spatially correlated channels, based on which an alternating optimization algorithm and a low-complexity algorithm with channel statistics based analog precoder are, respectively, proposed to optimize the hybrid precoder. Simulation results show the performance gain of the proposed hybrid precoder over the method that only considers data rate requirements or coverage constraints and also demonstrate the necessity of separating data and signaling planes at high frequencies.

Nonorthogonal Multiple Access and Carrierless Amplitude Phase Modulation for Flexible Multiuser Provisioning in 5G Mobile Networks

In this paper, a combined nonorthogonal multiple access (NOMA) and multiband carrierless amplitude phase modulation (multiCAP) scheme is proposed for capacity enhancement of and flexible resource provisioning in 5G mobile networks. The proposed scheme is experimentally evaluated over a W-band millimeter wave radio-over fiber system. The evaluated NOMA-CAP system consists of six 1.25-GHz multiCAP bands and two NOMA levels with quadrature phase-shift keying and can provide an aggregated transmission rate of 30 Gbit/s. The proposed system can dynamically adapt to different user densities and data rate requirements. Bit error rate performance is evaluated in two scenarios: a low user density scenario where the system capacity is evenly split between two users and a high user density scenario where NOMA and multiCAP are combined to serve up to 12 users with an assigned data rate of 2.5 Gbit/s each. The proposed system demonstrates how NOMA-CAP allows flexible resource provisioning and can adapt data rates depending on user density and requirements.

Slow adaptive energy‐efficient resource allocation in OFDMA HetNets considering demand uncertainty

AbstractIn this paper, we consider the downlink of a hybrid heterogeneous network with femtocells overlaid on a macrocell. Considering the rate requirements of femtocell users and interference thresholds for both co‐tier and cross‐tier users, we formulate the joint power and subchannel allocation problem to maximize the energy efficiency in orthogonal frequency‐division multiple access–based open‐access 2‐tier heterogeneous networks. Due to user's mobility and fluctuating data rate demand, data rate requirements of users are subject to uncertainty, which is ignored in previous works. Toward this end, we study the demand uncertainty by an error model in which femtocells do not have knowledge about the exact distributions and instead assume that each user demand follows a probability distribution function, which belongs to a set of distributions with specific properties (ie, mean and variance). Since these statistics are obtained from historical data, we also take into account the moment uncertainty. Under this error model, the distributionally robust energy‐efficient optimization problem is formulated, which is intractable. To make it tractable, we present the problem in an equivalent form by using the duality theory and then propose decomposition techniques to divide the problem into a fractional programming and a semi‐infinite subproblem. Moreover, we provide optimization methods to solve each one. Our extensive experimental results highlight the effectiveness of our robust solution in uncertain environments, examine the cost of robustness, and demonstrate the convergence of our proposed algorithm. As shown by simulations, our approach provides robustness while incurring minor loss (less than 5%) in optimality.

Group-based joint signaling and data resource allocation in MTC-underlaid cellular networks

Machine-type communications (MTC) are gaining significant research attention as one of the most promising technologies for the fifth generation (5G) mobile networks. A critical issue handled by MTC is support for massive numbers of connections, which is a growing problem that will become increasingly challenging as MTC share spectrum resources with cellular communication. Here, not only the number of connections but also the data rate requirements of cellular users (CUEs) need to be considered. Given these issues, in this paper, we formulate a group-basedjoint signaling and data resource optimization model constrained by network resource and data rate requirements in order to maximize the number of connections. We also note that this problem is nonconvex and that obtaining an optimal solution is computationally complex for MTC with massive numbers of users (UEs). Therefore, we decompose the problem into group-based data aggregation and resource allocation subproblems.To solve these two subproblems, we develop an adaptive group head selection algorithm and a joint signaling and data resource allocation algorithm that satisfy both the data rate requirement and resource constraints, respectively. Our simulation results show that our proposed algorithms significantly improve the number of connections when compared with other classic methods. Furthermore, our results reveal that thelimiting factor on the number of connections changes with the ratio of the number of MTC UEs to that of CUEs and the ratio ofdata requirement of MTC UEs to that of CUEs. Finally, we note that our proposed group-based resource allocation algorithm can effectivelyimprove the number of connections, especially when more MTC UEs and a small amount of MTC data are present.

Adaptive Zone Size Selection Method for IEEE 802.16j Mobile Multihop Relay Networks

IEEE 802.16j relay networks are capable of extending the coverage areas of IEEE 802.16 networks and can support the data rate requirements of users at cell edges. In these networks, each frame is divided into various zones to facilitate data transmission between various types of nodes. As the link qualities in the network change, accordingly the zone sizes should also change to support the Quality of Service (QoS) requirements of flows and to avoid bandwidth wastage. To address this issue, in this paper, we propose an adaptive zone size selection method for IEEE 802.16j networks. With this method, zone sizes of scheduling nodes are adaptively changed in accordance with their changing link qualities. As the link qualities degrade, the scheduling nodes may not be able to support the QoS requirements of flows due to the lack of free bandwidth availability in the corresponding zones. To handle this issue with less control overhead, we propose a cross-zone bandwidth allocation method. A zone size reselection method is also proposed to handle the situations where the cross-zone bandwidth allocation is not possible. Performance of the proposed method is compared with the performance of a fixed zone size selection method and a zone size selection method proposed in the literature. The proposed method shows significantly better performance than the other two methods in terms of traffic admittance, bandwidth utilization, and satisfaction ratios of flows.

Load Balancing Game With Shadowing Effect for Indoor Hybrid LiFi/RF Networks

Light Fidelity (LiFi) is a recently proposed technology that uses 300 THz visible light spectrum for high speed wireless communications as well as providing illumination. Basically, a LiFi access point (AP) covers only a few square meters, enabling a dense deployment of LiFi APs to improve the network throughput. However, channel blockage and shadowing in conjunction with inter-cell interference compromise the connectivity and system throughput of LiFi networks. In this paper, a network structure that combines LiFi with the conventional radio frequency (RF) system is considered. Users experiencing strong blockages can be switched to the RF system to achieve a higher data rate. In this paper, blockages, random orientation of LiFi receivers, and the user data rate requirement are characterised to model a practical communication scenario. A novel load balancing (LB) scheme based on evolutionary game theory is proposed for hybrid LiFi/RF networks. The performance of the proposed scheme is comprehensively analyzed. Results show that compared to state-of-the-art LB algorithms, the proposed scheme greatly improves user satisfaction levels at reduced computational complexity. Also, an optimal orientation of LiFi receivers and blockage density in hybrid networks would maximize the users quality of service.

Energy-Efficient Transmission Design in Non-orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) is considered as a promising technology for improving the spectral efficiency (SE) in 5G. In this correspondence, we study the benefit of NOMA in enhancing energy efficiency (EE) for a multi-user downlink transmission, where the EE is defined as the ratio of the achievable sum rate of the users to the total power consumption. Our goal is to maximize the EE subject to a minimum required data rate for each user, which leads to a non-convex fractional programming problem. To solve it, we first establish the feasible range of the transmitting power that is able to support each user's data rate requirement. Then, we propose an EE-optimal power allocation strategy that maximizes the EE. Our numerical results show that NOMA has superior EE performance in comparison with conventional orthogonal multiple access (OMA).

Forward and Backhaul Link Optimization for Energy Efficient OFDMA Small Cell Networks

This paper aims at improving the system energy efficiency of orthogonal frequency division multiple access small cell networks under user data rate requirements. Differently, in this paper, we take into account the effect of both forward link and backhaul link when formulating the system energy efficiency optimization problem. The optimal solution to the system energy efficiency problem needs the joint consideration of the scheduling, the transmission power optimization, and the backhaul link data rate control, which is NP-hard and generates huge signaling overhead. To this end, we first compute and obtain the lower bound and upper bound of the optimal system energy efficiency based on the property analysis of the formulated system energy efficiency problem. Then, to reduce the signaling overhead and calculation complexity, we propose a forward and backhaul link energy efficiency optimization scheme (FBEEOS) to approach the achieved system energy efficiency bounds. The system energy efficiency is proved to be convergent in a non-decreasing manner in FBEEOS. The simulation results confirm the properties of FBEEOS. In addition, our results show that the transmission power optimization is necessary to achieve higher system energy efficiency though its contribution to total energy consumption is negligible. This is a result of the effect of transmission power on interference and throughput, and needs to be taken into consideration when we optimize the system energy efficiency.

Resource allocation in OFDMA heterogeneous networks for maximizing weighted sum energy efficiency

In this paper, a resource allocation algorithm for maximizing the weighted sum energy efficiency (EE) is investigated in orthogonal frequency division multiple access (OFDMA) heterogeneous networks (Het-Nets). We aim to balance the EE of macro cell and low power nodes by subchannel and power allocations. We formulate the problem as a nonlinear sum-of-ratios programming issue, and guarantee data rate requirements of users by using minimum rate constraints. Due to the nonconvexity of the problem, we develop a heuristic subchannel assignment algorithm, and then solve the power allocation problem by parameterized transformations and a first-order approximation based on an iterative algorithm. Numerical results illustrate the convergence and the effectiveness of the proposed algorithm.