Table 4.
Characteristics of selected resource allocation techniques in 5G.
Ref | Algorithm/Scheme/Strategy | Problem Addressed | Improvements/Achievements | Limitations/Weakness |
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[89] | Cooperative Online Learning Scheme | Extreme interference between the multi-tier users. |
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[90] | Game-theoretic approach | Cross-tier interference. |
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[91] | Genetic Algorithm Particle Swarm Optimization-Power Allocation (GAPSO-PA) | The allocation of power in heterogeneous ultra-dense networks. |
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[92] | Estimation of Goodput based Resource Allocation (EGP-BASED-RA) | Enhance Goodput (GP): (a specific metric of performance). |
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[93] | The social-aware resource allocation scheme | D2D multicast grouping; |
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Ineffective D2D links. |
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[94] | PGU-ADP algorithm | Dynamic virtual RA problem. |
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Expansion of the total user rate. |
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[95] | Efficient Resource Allocation Algorithm | Enhance system capacity and maximum computational complexity. |
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[96] | GBD Based Resource Allocation Algorithm | Enhances allocating algorithm’s efficiency. |
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[97] | Multitier H-CRAN Architecture | Lacking intelligence perspective using existing C-RAN methods. |
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[98] | Bankruptcy game-based algorithm | Resource allocation and inaccessibility of wireless slices. |
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[99] | BVRA-SCP Scheme | Enhancing service demands like low latency, enormous connection, and maximum data rate. |
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[100] | VNF-RACAG Scheme | Settlement of virtualized network functions (VNF). |
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[101] | Hybrid DF-AF scheme | Promising to incorporate various wireless networks to deliver higher data rates. |
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[102] | Cooperative resource allocation and scheduling approach | Scheduling and resource allocation problems. |
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[103] | SWIPT framework | Low energy efficiency and high latency. |
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[104] | The device-centric resource allocation scheme | Declining of network throughput and raises delay in resource allocation. |
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[105] | Distributed Resource Allocation Algorithm | Resource allocation and interference management in 5G networks. |
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[106] | Unified cross-layer framework | Physical layer modulation format and waveform, resource allocation, and downlink scheduling. |
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[107] | Dynamic joint resource allocation and relay selection scheme | Relay selection and downlink resource allocation. |
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[108] | Low-Complexity Subgrouping scheme | Radio resource management of multicast transmissions. |
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[109] | Joint Edge and Central Resource Slicer (JECRS) framework | Requires distinct resources from the lower tier and upper tier. |
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[110] | TCA algorithm | MTC devices are battery restricted and cannot afford much power consumption needed for spectrum usage. |
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[111] | IHM-VD algorithm | Power allocation and channel allocation issue. |
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[112] | Centralized approximated online learning resource allocation scheme | The inter-tier interference among macro-BS and RRHS; and energy efficiency. |
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[113] | Spectrum resource and power allocation scheme | Emphasize on a fair distribution of resources in one cell. |
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[114] | Tri-stage fairness scheme | Resource allocation problem in UDN having caching and self-backhaul. |
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[115] | Fronthaul-aware software-defined resource allocation mechanism | Overhead generated using a capacity-limited shared fronthaul. |
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[116] | Heterogeneous statistical | Heterogeneity issues. |
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The QoS-driven resource allocation scheme |
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[117] | Nondominated sorting genetic algorithm II (NSGA-II) | Unable to get optimal results concurrently. |
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[118] | Joint access and fronthaul radio resource allocation | Downlink energy efficiency (EE) and millimeter-wave (MMW) links in access and fronthaul. |
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[119] | Double-sided auction-based distributed resource allocation (DSADRA) method | Intercell and inter tier interference. |
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[120] | Joint power and reduced spectral leakage-based resource allocation | Interference from D2D pairs. |
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[121] | Branch-and-bound scheme | Latency-optimal virtual resource allocation. |
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[122] | The learning-based resource allocation scheme | To achieve high system capacity better performance in terms of effective system throughput. |
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[123] | Resource allocation method with minimum interference for two-hop D2D communications | Interference which reduces network throughput. |
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[124] | Multiband cooperative spectrum sensing and resource allocation framework | Energy consumption for spectrum sensing. |
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[125] | Channel-time allocation PSO Scheme | To acquire gigabit-per-second throughput and low delay for achieving and maintaining the QoS. |
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[126] | Heterogeneous (high density)/hierarchical (low density) virtualized software-defined cloud RAN (HVSD-CRAN). | Density of users. |
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[127] | Mini slot-based slicing allocation problem (MISA-P) model | The probability of forming 5G slices. |
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[128] | A joint resource allocation and modulation and coding schemes | Requirement of extremely low latency and ultra-reliable communication. |
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[129] | QoS/QoE-aware relay allocation algorithm | Neglects temporal requirements for optimum performances. |
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[130] | The learning-based resource allocation scheme | Interference coordination complexity and significant channel state information (CSI) acquisition overhead. |
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[131] | Device-to-device multicast (D2MD) scheme | Improving spectrum and energy efficiency and enabling traffic offloading from BSs to device. |
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[132] | Constrained deferred acceptance (DA) algorithm and a coalition formation algorithm | The interference management among D2D and current users. |
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[86] | Novel resource allocation schemes (hybrid resource management) | Energy efficiency and consumption. |
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[133] | Orthogonal multiple access (OMA) and relay-assisted transmission schemes. | Jointly optimize the block length and power allocation for reducing error probability. |
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[134] | Joint user association and Power Control algorithm | Optimizing power control and user association schemes. |
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[135] | Successive convex approximation (SCA) based alternate search method (ASM) | Raise the total sum rate of users. |
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[136] | An online learning algorithm for resource allocation | Inter-tier interference among RRHS and macro-BSs, and energy efficiency. |
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[137] | Joint resource block (RB) and power allocation scheme | Enhance fairness in data rate among end-users. |
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[138] | Hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) | Achieve the SE-EE tradeoff having minimum rate requirement of each user. |
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[139] | Stackelberg game model | High inter-cell interference (ICI) and less energy efficiency. |
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[140] | Virtual code resource allocation (VCRA) approach | Reducing the collision probability. |
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[141] | Deep reinforcement learning -unicast-multicast resource allocation framework (DRL-UMRAF) | High-quality services and achieving green energy savings of base stations. |
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[142] | Deep reinforcement learning-based intelligent Up/Downlink resource allocation | The high dynamic network traffic and unpredicted link-state change. |
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[143] | Joint computation offloading and resource allocation scheme | Complete network information and wireless channel state. |
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[144] | Deep neural network-Multi objective Sine Cosine algorithm (DNN-MOSCA) | Achieving better accuracy and reliability. |
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[145] | The improved resource allocation algorithm | Improving QoS requirements in MTC. |
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[146] | Resource Allocation Algorithm | The interference to 5G cellular users (CUs) related to QoS. |
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[147] | Genetic algorithm- intelligent Latency-Aware Dynamic Resource Allocation Scheme (GI-LARE) | Efficient radio resource management. |
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[148] | A Low-complexity centralized packet scheduling algorithm | Downlink centralized multi-cell scheduling. |
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[149] | Smart queue management method | QoS of end-to-end real-time traffic. |
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[150] | Proposed Optimal Resource Allocation Algorithm | The optimization problem in mixed-integer nonlinear programming (MINLP). |
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[151] | A novel packet delivery mechanism | Issues related to using CoMP for URLLC in C-RAN architecture. |
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[152] | Distributed joint optimization algorithm for user association and power control | Improve total energy efficiency and reduce the inter-cell and intra-cell interference. |
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[153] | Pollaczek–Khinchine formula based quadratic optimization (PFQO) | Inaccurate transmission recovery delay of URLLC multi-user services. |
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[154] | An outer approximation algorithm (OAA) | Multiple interferences, imbalanced user traffic load. |
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[155] | Joint Power and Subcarrier Allocation | URLLC reliability and network spectral efficiency. |
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[156] | Weighted Majority Cooperative Game Theory Based Clustering | Increase interference, improper utilization of resources. |
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[157] | Bee-Ant-CRAN scheme | Design a logical joint mapping among RRHS and User Equipment (UE) and RRHS and BBUS too. |
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[158] | Noncooperative game theory-based user-centric resource optimization scheme | Enhance the coverage probability and sum rate. |
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