Macro Base Station Research Articles

Comparison of Different Frequency Channel Allocation Methods in ultra-dense networks

The impact of information transmission on production and life under dense networking is self-evident. Dense networking application scenarios include airports, dense residential buildings, dense business districts and blocks, campuses, large gatherings, stadiums, subways, etc. However, with the increase of 5G frequency band, the 5G macro base station signal attenuates more when it penetrates the wall than 4G, which makes it difficult to cover indoor signals. The outdoor 5G macro base station signal can only provide shallow indoor coverage after penetrating obstacles such as brick walls, glass and cement, which cannot guarantee the good experience required for indoor deep coverage. This paper compares and discusses various channel allocation algorithms based on user scenarios in ultra-dense networks. By adjusting some parameters of 19 units, the integer frequency reuse (IFR) method (N=1,3), fractional frequency reuse (FFR) method, simultaneous water injection (SWF) method, and transmission interference temperature limit (TX-ITL) method are compared. In order to increase the capacity, a combination of several algorithms will also be used.

Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network

The heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From a communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy-aided networks offer to curtailed fossil fuel consumption, so to reduce the environmental pollution. This paper proposes a renewable energy based power supply architecture for the off-grid HetNet using a novel energy sharing model. Solar photovoltaics (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, a biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel-weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication delays. Furthermore, the proposed algorithm with an extended discontinuous reception (eDRX) and power saving mode (PSM) for narrowband internet of things (IoT) applications extends the battery lifetime for IoT devices. HOMER optimization software is used to perform optimal system architecture, economic, and carbon footprint analyses while the Monte-Carlo simulation tool is used for evaluating the throughput and energy efficiency performances. The proposed algorithms are validated through the practical data of the rural areas of Bangladesh from which it is evident that the proposed power supply architecture is energy-efficient, cost-effective, reliable, and eco-friendly.

Blockchain-escorted distributed deep learning with collaborative model aggregation towards 6G networks

The 6th generation (6G) network targets the Internet of Everything (IoE) implementation, and Distributed Deep Learning (DDL) can promote this progress with innovative performance in generating intelligence. Meanwhile, the 6G networks support ultra-reliable and low-latency communication (uRLLC) and thus can further elevate the DDL performance to empower the IoE development. However, DDL designs mostly focus on individual areas and yield separate intelligence which is insufficient for the IoE; besides, DDL platforms are usually managed in the centralized fashion, which is vulnerable for data preservation and task execution; the complexity of 6G networks involving heterogeneous devices and relations aggravates issues about reliability and efficiency of DDL. To this end, we propose a novel BC-escorted 6G-based DDL design for trustworthy model training. In this system, the 6G network design is utilized for system-wide uRLLC; non-homogeneous edge devices are grouped up with weighted consideration for DDL to train CNN models; macro base stations (MBSs) and small base stations (SBSs) jointly provide two-tiers parameter aggregation to elevate the knowledge level; a dual-driven BC consensus is designed to verify tasks and models; users anyplace can retrieve models via the BC nodes for object detection. The proposed design is evaluated in comparison with Cloud-based and P2P-based DDLs, and the results demonstrate better performance on accuracy and latency achieved in the proposed system.

Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks

The dense deployment of small-cell networks is a key feature of the next-generation mobile networks employed to provide the necessary capacity increase. The small cells are installed in the areas covered by macro base stations (eNBs) to supply the required local capacity based on the known concept of the hierarchical HetNets. Moreover, small-cell networks use high-capacity backhaul links on millimeter-wave bands to develop multihop topologies to mitigate the costs of data transmission. Nonetheless, green networking gains great importance for the uncontrolled installation of too many small cells may escalate operational costs and emit more carbon dioxide. This article proposes a dynamic optimization model to minimize the overall energy consumption of fifth-generation (5G) heterogeneous networks and provide the essential coverage and capacity. Optimizing carrier allocation and power utilization, the proposed model determines when to turn <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> or <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> small cells to meet the quality of service constraints of users with the highest level of energy efficiency. We also proposed a multihop backhauling strategy to effectively use the existing infrastructure of small-cell networks for simultaneous dual-hop transmissions. The numerical results indicated considerable rates of power saving in different traffic models while guaranteeing the throughput requirements for uniform and hotspot user equipment distribution patterns. Also, according to the simulation results, energy efficiency and system data rates can significantly be improved.

Efficient Online Learning Based Cross-Tier Uplink Scheduling in HetNets

Heterogeneous cellular networks (HetNets), where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs), can significantly improve the spectrum efficiency by Pico- and Macro base station collaboration. Due to cross-tier interference, joint detection of uplink signals is widely adopted so that Pico-BS can either detect the uplink signals locally or forward them to Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process. We propose an efficient algorithm, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> , that can deal with the exponentially growing state and action space. Furthermore, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> is online learning-based which does not require any prior knowledge on user behavior or channel characteristics. We prove the convergence of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> and derive an upper bound on its approximation error. Extensive experiments in various scenarios show our algorithm outperforms existing methods in reducing delay and power consumption.

SINR Meta Distribution for mmWave Heterogeneous Networks Under Varying Queue Status: A Spatio-Temporal Analysis

A fine-grained analysis of network performance is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as a Poisson point process (PPP). By utilizing stochastic geometry and queueing theory, we characterize the spatial and temporal randomness while the special characteristics of mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming, are incorporated into the analysis. We derive the moments of the conditional successful transmission probability (STP). By taking the temporal random arrival of traffic into consideration, an equation on the meta distribution is formulated and its solution is obtained in a recursive manner. The critical arrival rate for a fraction of the queues at the BSs to be stable is provided. In addition, the mean delay is obtained. The numerical results reveal the impact of the key network parameters, such as the blockage parameter, the bias factor, the number of antenna elements and the BS density, on the network performance. Specifically, offloading the users from the macro BS tier to the small BS tier by biasing can reduce the performance fluctuation while only causing a slight degradation of the overall network performance.

A novel power consumption optimization framework in 5G heterogeneous networks

The fifth-generation (5G) mobile networks have the capacity to handle the dynamic traffic demands of the user equipment (UE). One approach is the dense deployment of small base stations (s-BSs), which can control the dynamic traffic demands. We consider s-BSs which are interconnected through the mmWave backhaul (BH) link to transfer traffic from the s-BS to the core network through the macro base station (MBS). In this setting, the network power consumption is affected by the way UEs are connected to the base stations and the traffic routes through the BH links. The main objective of this paper is to minimize the power consumption of the heterogeneous network (HetNet) with the intelligent backhauling and s-BS/BH link sleeping (IBSBS) framework. The proposed framework provides the minimized power consumption in HetNets through UE association, backhauling, and sleep s-BS/BH links. The UE association and backhauling uses a heuristic function based intelligent backhauling algorithm to assess the minimum power consumption from the s-BS to the MBS while considering the power and capacity constraints of the network. The load sharing based s-BS sleeping algorithm dynamically changes the states (active/sleep) of the s-BSs according to their loads without compromising UE demands. Different distributions of UEs and different data rates over the HetNets architecture are considered for performance evaluation. The evaluation results of the proposed framework outperform the state-of-the-art algorithms in terms of network energy efficiency, power consumption, and the number of active s-BSs/BH links.

Joint cell zooming and sleeping strategy in ultra dense heterogeneous networks

To optimize the ultra dense distribution of base stations (BSs) which consist of micro BS and macro BS, balancing the tradeoff between user communication quality and energy saving is essential. In this paper, a joint cell zooming and sleeping strategy is proposed for the downlink two-tier heterogeneous network under the Poisson Point Process (PPP) model, in order to balance the user benefits and communication system operating cost. First, the cell zooming scheme is presented based on game theory, where the association probability of each base station is achieved by adjusting the cell zooming factor (CZF). With the improved BS association probability, more users can benefit with better network experience by connecting to micro BS instead of macro BS. Then a two-step sleeping strategy is put forward, by applying the cell zooming. The first step is to ascertain which BSs are kept based on the analysis of the energy consumption of candidates. The second step is used to measure the overall overlapping degree of each BS and provide different sleep decisions according to the radius of the area covered by MBS. The proposed sleeping scheme is able to stimulate the BS with a high overlapping degree to be slept to achieve more energy saving. Through the proposed joint schemes, the service efficiency of communication system is improved, when the users connect to micro BS instead of macro BS and some micro BSs with low utility are turned off. Simulation results indicate the improved performance in terms of the user experiences and energy saving.

Total Delay Optimization in Cache-Enabled C-RANs With Hierarchical Rate Splitting

This paper proposes a Hierarchical Rate Splitting (HRS)-based and Macro Base Station (MBS)-assisted cooperative content delivery scheme for Cache-enabled Cloud Radio Access Networks (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RANs). In this scheme, a user is served by a cluster of Small Base Stations (SBSs) that cache contents. If the user-requested content is missing in its cache, the SBS retrieves it from the central processor (backhaul link) and the MBS (wireless broadcast). We minimize the total delay of this system. We thus optimize user grouping, decoding order, RS variables, BS clustering, and beamforming subject to the transmit power and backhaul capacity constraints. This process formulates a non-convex problem, which we decouple into three subproblems. First, we must pair users to reduce intra-group interference. We thus develop a Low-Complexity User Pairing (LCUP) algorithm. Second, we optimize the decoding order to minimize the broadcast link delay. We, therefore, propose a Dynamic Delay-based Decoding Order Update (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> OU) algorithm. Third, we jointly design the RS variables, beamforming, and BS clustering. We use surrogate optimization via the Quadratic Transform (QT), Woodbury matrix identity, and Taylor expansion. We then recast this problem to a Two-Tier Alternating (TTA) problem, which we solve with closed-form (outer tier) and convex (inner tier) formulations. We integrate these algorithms to propose an overall algorithm. It achieves significant performance gains and a 3-fold reduction in time-complexity over the standard Semi-Definite Relaxation (SDR)-based algorithm and several benchmark schemes.

Secure Information Transmission for B5G HetNets: A Robust Game Approach

This article investigates the robust secure transmission problem in two-tier B5G heterogeneous networks with multiple noncollusive eavesdroppers and users, where two types of imperfect channel state information (CSI) scenarios, i.e., instantaneous and statistic CSI scenarios, are considered. Given the two-sidedness of co-channel interference in physical-layer security and the selfishness of femtocell base stations (FBSs), an imperfect-CSI-based noncooperative game framework is proposed to maximize the profits of the macro base station (MBS) and FBSs, while guaranteeing user’s Quality-of-Service (QoS) requirement in terms of outage probability. Specifically, based on the involved two CSI scenarios, the original game where the MBS and FBSs act as players is elaborated as two robust game problems. To address channel uncertainties in the objective function, the worst-case and the mean value of the channel gains are used separately. Besides, the remaining channel uncertainties embodied in the intractable outage probability constraints are treated in a unified way, i.e., the extended Bernstein approximation. The existence and uniqueness of the Nash equilibrium (NE) are analyzed, and the sufficient condition on the uniqueness of the NE is derived. Then, two robust iterative algorithms are given to approach the robust game equilibrium. Finally, numerical results are presented to verify the theoretical analysis and show the robustness and effectiveness of the proposed algorithms.

Risk Area Alert Through Heterogeneous Mobile Networks: A New Approach to Fight COVID-19 and Beyond

COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this article, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID-19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end.

Coverage Analysis of mmWave and THz-Enabled Aerial and Terrestrial Heterogeneous Networks

Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of a macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)-enabled unmanned aerial vehicles (UAVs) operating at 28 GHz, and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper. Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. We show that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.

Detection of Cache Pollution Attack Based on Federated Learning in Ultra-Dense Network

Ultra-Dense Network (UDN) as one of the key technologies of 5G aims to increase the capacity per area of the current 4G network by 1,000-fold through the deployment of dense Small Base Stations (SBSs). To reduce the traffic on backhaul links, Content-Centric Networking (CCN) as a promising network architecture has been applied to UDN recently. However, its in-network caching mechanism is subject to Cache Pollution Attack (CPA), where attackers aim to occupy limited caching space with unpopular contents. Existing studies focusing on the static networks cannot be directly used in dynamic UDN scenarios. In this paper, we propose a detection and defense scheme of CPA based on federated learning, where CPA is determined by the cooperation of multiple SBSs. First, each SBS can build or join a cluster based on the metrics of distance similarity and load similarity. Then, each cluster head as the working node trains the local classifier to detect CPA based on the statistics within the cluster. The macro base station aggregates all the local models to generate an improved global classifier for all the SBSs. In our scheme, federated learning as a special case of distributed machine learning allows each cluster head to independently train the local data even when it is non-i.i.d. or unbalanced. Simulation results show that our scheme outperforms in terms of detection ratio, cache hit ratio, access delay, and false alarm ratio compared with other state-of-the-art schemes.

Energy-Minimized Partial Computation Offloading for Delay-Sensitive Applications in Heterogeneous Edge Networks

Mobile Devices (MDs) support various delay-sensitive and computation-intensive applications. Yet they only have limited battery energy and computing resources, thereby failing to totally run all applications. A mobile edge computing (MEC) paradigm has been proposed to provide additional computation, storage, and networking resources for MDs. Servers in MEC are often deployed in both macro base stations (MBSs) and small base stations (SBSs). Thus, it is highly challenging to associate resource-limited MDs to them with high performance, and realize partial computation offloading among them for minimizing total energy consumption of an MEC system. To tackle these challenges, this work proposes a novel computation offloading approach for delay-sensitive applications with multiple separable tasks in hybrid networks including MDs, SBSs, and an MBS. To achieve it, this work formulates total energy consumption minimization as a constrained mixed integer non-linear program. To solve it, this work designs an improved meta-heuristic optimization algorithm called <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> article swarm optimization based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> enetic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning (PGL), which integrates strong local search capacity of a particle swarm optimizer, and genetic operations of a genetic algorithm. PGL jointly optimizes task offloading among MDs, SBSs, and MBS, users’ connection to SBSs, MDs’ CPU speeds and transmission power, SBSs and MBS, and bandwidth allocation of available channels. Simulations with real-world data collected from Google cluster trace demonstrate that PGL significantly outperforms other existing methods in total energy consumption of an entire system.

Miniaturized, Vertically Polarized, Pattern Reconfigurable Dielectric Resonator Antenna and Its Phased Array for Wide-Angle Beam Steering

This article presents a novel metamaterial-inspired, miniaturized, vertically polarized (VP), pattern reconfigurable dielectric resonator (DR) antenna/phased array for sub-6 GHz fifth-generation (5G) beam-steering applications. The proposed antenna is miniaturized by creatively loading the mushroom structure on the DR, and the beam steering/scanning function is realized using a pattern reconfigurable configuration. Owing to the extra loading capacitance and inductance from the VP mushroom, the resonance frequency of the DR shifts to the lower region. Based on the classical Huygens radiation principle and image theory, a novel miniaturized Huygens form with a tilted pattern is developed. Then, by exploring a switchable magnetic current source, a pattern reconfigurable Huygens design is proposed and studied. For verification, a pattern reconfigurable DR antenna with eight beams and a low-cost 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times4$ </tex-math></inline-formula> wide-angle beam-scanning phased array using the reconfigurable DR cells for 5G microcell and macro-base station demands are fabricated and measured, respectively. The measured −10 dB impedance bandwidth and peak gain are 3.30–3.75 GHz (12.8%) and 4.8 dBi, respectively. Also, the proposed 2-D phased array can scan up to ±60° and supports a 3 dB beam coverage up to ±80° in each region. The proposed beam-steering antenna/array is a promising candidate for 5G microcell and macro-base station applications.

HOPCTP: A Robust Channel Categorization Data Preservation Scheme for Industrial Healthcare Internet of Things

In this article, we present a robust channel categorization scheme to fix data privacy and preservation problems in an Industrial Healthcare Internet of Things (IHC-IoT) network. The proposed model categorizes the transmission bandwidth into four independent channels for each device by defining triggering rules with respect to time for reception and transmission of data. Besides, our developed prototype, which is known as high optimal path channel triggering protocol (HOPCTP) ensures data privacy and preservation utilizing minimal network resources in an IHC-IoT network. Furthermore, the HOPCTP prototype enables client-side devices to transmit and receive data with four different independent communication channels following the triggering mechanism to avoid adversary device anticipation in the network. The categorized channels are triggered with a defined time period to change their transmission and reception functionality, which triggers the transmitted data between different channels. Same data transmission via four different channels ensures the confidentiality and integrity of data because if an attacker captures one channel of data, he will not be able to understand the full message. The convalescent communication infrastructure is developed among patient wearable devices followed by cluster heads, micro base station, and macro base station to ensure data privacy and preservation with better communication metrics. In addition, the objective of the HOPCTP prototype is to resolve the data privacy issues in delay-sensitive applications, i.e., IHC-IoT networks. To achieve this, the HOPCTP prototype promotes data preservation and communication in terms of authenticity, congestion, throughput, communication cost, and packet loss ratio. The result statistics of the proposed scheme demonstrate remarkable improvement over the existing schemes for aforementioned comparative metrics.

MEC-Based Cooperative Multimedia Caching Mechanism for the Internet of Vehicles

Multimedia applications are expected to widely deploy over vehicular networks. In order to meet the low-latency and high-speed transmission requirements of multimedia applications, edge caching is introduced to reduce the network traffic and the transmission delay. Due to the limited storage of the edge cache server, an efficient approach for the content management plays a decisive role for the edge cache performance. This paper proposes a vehicle-to-infrastructure-based cooperative caching mechanism for Internet of Vehicles to improve the edge cache utilization. The system model is established with the goal of maximizing the cooperative caching hit rate. To jointly consider the collaborations between macrobase stations (MBS) and multiple roadside units (RSU), we propose a reinforcement learning algorithm to adaptively control the cache management. According to the content popularity and the network status, the proposed algorithm can dynamically adjust cached content across relevant MBSs and RSUs. The simulation results show that the proposed cooperative caching mechanism significantly improve the cache utilization and the quality of services.

Latency Minimization for Secure Intelligent Reflecting Surface Enhanced Virtual Reality Delivery Systems

This letter investigates a virtual reality (VR) delivery system, where the original VR contents requested by all users are stored at the macro base station (MBS). To reduce latency, MBS can either transmit the original VR data or the computed VR data to multiple users aided by an intelligent reflecting surface (IRS) to prevent attacks from an eavesdropper with imperfect channel state information (CSI). We jointly optimize the transmission policies, MBS transmit power, IRS phase shift and computing frequency to minimize the latency over all users subject to security constraint. Numerical results validate the robustness of our proposed algorithm.

Coverage Analysis of Vehicular Safety Messages-Prioritized C-V2X Communications

For safety applications in intelligent transportation system (ITS), it is essential for vehicles and pedestrians to decode the safety messages from nearby moving vehicles through direct sidelink in the presence of cellular link. This article presents the coverage probability analysis of vehicular safety messages-prioritized cellular vehicle-to-everything (C-V2X) communications. We model the spatial layout of macro base stations (MBSs) and vehicles as a 2-D Poisson point process (PPP) and a Poisson line Cox point process (PLCPP). Since vehicles can be regarded as mobile base stations, we assume that the MBSs and vehicles share the same spectrum. In a similar way, we consider two kinds of users, i.e., planar users and linear users, which are also modeled by a 2-D PPP and a PLCPP, respectively. Using a stochastic geometry tool, we derive the signal-to-interference ratio (SIR)-based coverage probability of four links (i.e., downlink of planar user, sidelink of planar user, downlink of linear user, and sidelink of linear user) according to the vehicle-prioritized association scheme. We assume that the users are required to decode the vehicular safety messages if they are within a certain distance from vehicles. Then, we derive the conditioned coverage probability of four links to study their reliability. In addition, we explore the impacts of several key parameters on the coverage probability and provide some design insights.

Hybrid NOMA-FDMA Assisted Dual Computation Offloading: A Latency Minimization Approach

Edge computing has been considered as a promising solution for enabling computation-intensive yet latency-sensitive applications at resource-constrained wireless devices (WDs). In this paper, exploiting the advanced small-cell dual connectivity (DC), we investigate a paradigm of dual computation offloading in which a WD can simultaneously offload partial workloads to a cloudlet-server co-located at the macro base station (MBS) and an edge-server (ES) co-located at a small-cell based station (SBS). To facilitate the multi-user dual computation offloading, we exploit a hybrid model of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA). Specifically, due to the SBSs’ limited channel resources, we consider that the WDs form different NOMA-groups for offloading their respective workloads to different SBSs, which improves the spectrum efficiency. Meanwhile, all WDs use FDMA for offloading their workloads to the MBS, which avoids the WDs’ co-channel interference. We formulate a joint optimization of the WDs’ partial offloading decisions, their FDMA transmission to the MBS, different NOMA-groups’ transmission to the SBSs, as well as the computing-rate allocation of the ESs and the cloudlet-server, with the objective of minimizing the overall latency for completing all WDs’ workloads. Despite the strict non-convexity of the joint optimization problem, we propose a layered yet cell-based distributed algorithm for obtaining the optimal dual offloading solution. Based on the optimal dual offloading solution, we further investigate how to properly group WDs into different NOMA-groups for offloading workloads to the corresponding SBSs, and propose a cross-entropy based learning algorithm for finding the optimal NOMA grouping scheme. Numerical results are finally provided to validate the effectiveness and efficiency of our proposed algorithms.