Resource Allocation Schemes for 5G Network: A Systematic Review
Abstract
:1. Introduction
2. Background
3. Related Work
4. Design of Research
4.1. Research Questions
- What are the existing state-of-the-art challenges in 5G?
- What is the importance of resource allocation in 5G?
- Which current policies, strategies, and algorithms are being used for resource allocation in 5G?
- Which metrics and parameters are considered during resource allocation in 5G?
- Which open issues and research trends are unaddressed in resource allocation in 5G?
4.2. Search Criteria
4.3. Data Sources
4.4. Article Selection Process
4.5. Inclusion and Exclusion Criteria
5. Discussion
5.1. Q1. What Are the Existing State-of-the-Art Challenges in 5G?
- Deployment of MIMO: 5G will require a paradigm shift that incorporates a huge bandwidth having very high-frequency spectra as a carrier, excessive densities of a base station, and a remarkable number of antennas to provide for the massive growth on the behalf of the increased amount of traffic.
- mm-Wave: Millimeter waves are transmitted with frequencies between 30 and 300 GHz, compared with the bands traditionally used for mobile devices, which are below 6GHz. This technology guarantees huge data capacity as compared with the one that is currently being used. However, mm-waves face one main drawback—i.e., traditionally, a higher range of frequencies is not sufficient for outdoor applications due to blockage and high propagation loss caused by rain and tall buildings [70].
- Pilot contamination and channel estimation/feedback: Channel State Information (CSI) is critical for attaining the benefits of multi-antenna in MIMO systems. CSI has become more demanding in massive MIMO systems because of the massive number of antennas. Furthermore, a massive MIMO system needs a massive number of pilots for both times-division duplexing (TDD) and frequency-division duplexing (FDD) [71].
- The trade-off between computation power and transmission power: Across the 5G network, an additional BS’s power relies on the transmission and computation power of the BSs. When extra power is added to BSs and combined with the transmission and computation power of the additional BS, then the 5G network’s energy efficiency is also calculated by the BSs’ transmission and computation energy [72].
- Mobility: 5G networks require work with speed up to 1000 km/h [73,74]. A substantial investigation is needed to uncover the issues related to the selection of optimum beam and the development of methods/schemes that enhance the requirement for the response for CSI to the transmitter. Thus, massive MIMO performance is delicate with regard to speed because this the computational load can make multiuser solutions unaffordable [41].
- Mixed-Numerology interference: As per the divergent demands of mMTC and URLLC, service configuration contrasts vigorously from the perspectives of the physical layer [75]. Specifically, mMTC is characterized by a sampling rate having a low baseband for supporting huge connectivity and by a small sub-carrier spacing with narrowband transmission, reduced consumption of power, and extensive coverage having low-cost. On the contrary, URLLC mostly requires spacing for many subcarriers to deal with the rigorous requirement for latency and sampling rate for high baseband. These diverse configuration discrepancies in RF and baseband predictably lead to considerable interference [76,77] in crucial mMTC.
- 5G UE’s testing challenges: The problems that appeared in testing 5G UE are like the issues that happened in traditional systems having power control, extreme power output, and sensitivity behavior of receiver as measurement matrices. Therefore, the demand for using SC-FDMA for the uplink and an OFDMA scheme in the downlink in LTE-A/LTE-B based 5G systems, along with assistance for instantaneous links having harvesting capabilities for energy provisioning, need novel ideas of measurement for supporting required trials. For suitable RF measurements, trial equipment must automatically consider operating signaling protocols that utilize parameters defined by the user (such as a channel number). The UE operational testing should integrate the signaling protocol, handover testing, and end-to-end throughput. The main challenge faced in 5G UE testing is to guarantee that the response of state-change requirements is met [78].
- Dynamic heterogeneous resource optimization: It is difficult to endorse data transmission efficiency for the services getting URLLC as a top priority in mMTC. Due to the lack of radio resources, it is mandatory to consider their co-presence by combining their conflicting requirements and specifications concerning latency, reliability, density, and bandwidth. Therefore, the efficient arrangement of the resources in the wireless environment using dynamic and intelligent ways across various stages of service requirements is a demanding job [79].
- Efficient and realistic measurement: As data measurement is critical for the required modification/extension of current transmission models, the approach to measurement should cover various ranges of frequencies, spherical waves, 3D (elevation), and spatial consistency, along with new paradigms of communication, such as small cell and M2M/D2D communications. Furthermore, measurements must be captured for mm-wave (i.e., 60 GHz and over) for outdoor and indoor criteria, and they must feasibly apply to real-life scenarios (such as vehicle-to-vehicle/roadside communication, crowded areas, etc.) [80].
- Isolation among Network Slices: In a 5G network, many services have unique requirements. Consequently, the resources of a dedicated virtual network are required to certify the quality of service at every slice. A network needs high-performance slices isolated from each other. Through control plane and data plane isolation this isolation of network slices can be achieved. Generally, the slice control function can be distributed between various slices, whereas in some of the services, such as mission-critical communications, the resource sharing provides various benefits for infrastructure benefactors while it brings some challenging issues such as slice isolation. Network slices require control functionality. Moreover, the effective isolation of each network slice confirms that a security attack or any other failure does not alter another slice’s operation. Therefore, the mechanism of slice isolation is a predominant challenge while employing network slicing [81].
- Privacy protection: Obscurity services of 5G demands much more attention when compared with the previous cellular networks. The exclusive data rate of 5G carries a huge amount of data flow that contains private and sensitive information such as identity, private content, and position. In certain situations, the breach of privacy may lead to extreme consequences. For example, unintentional release of personal health data may expose the private information of a person, while the release of routing data for a vehicle may reveal its position to unauthorized others [82]. Due to the an application’s privacy requirements, the protection of privacy is a challenging issue faced by 5G wireless networks.
- Coordinated multiple points (CoMP): CoMP having 5G massive MIMO will play a critical part in enhancing the quality of communication, coverage, EE, and throughput of the network [83]. Moreover, mobile users can use relatively higher quality and better performance when located in another cell zone. Therefore, the CoMP system having 5G massive MIMO still has some open challenges—such as backhauling, processing, and cooperative framework—that demand more attention and study, in turn, to achieve maximum benefits of the network for the operator while keeping the cost in control.
5.2. Q2. What Is the Importance of Resource Allocation in 5G?
5.3. Q3. Which Current Policies, Strategies, and Algorithms Are Being Used for Resource Allocation in 5G?
5.4. Q4. Which Metrics and Parameters Are Considered during Resource Allocation in 5G?
5.5. Q5. Which Open Issues and Research Trends Were Unaddressed in Resource Allocation in 5G?
6. Open Research Issues and Trends in 5G
6.1. Joint Resource Allocation Techniques
6.2. Fronthaul/Backhaul/C-RAN Issues
6.3. Minimization of Latency
6.4. Energy Efficiency
6.5. Network Scalability
6.6. Mobility Management
6.7. Management of Services
6.8. Network Virtualization
6.9. Appropriateness in Practical Situations
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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String | B1 | B2 |
---|---|---|
String 1 (S1) | Fifth Generation | Resource Allocation |
String 2 (S2) | Fifth Generation | Resource Distribution |
String 3 (S3) | Fifth Generation Network | Resource Reservation |
String 4 (S4) | 5G | |
String 5 (S5) | 5G Network |
Publisher | URL |
---|---|
MDPI | https://www.mdpi.com (accessed on: 1 July 2021) |
Science Direct | https://www.sciencedirect.com (accessed on 15 June 2021) |
Wiley Online Library | https://onlinelibrary.wiley.com (accessed on 20 June 2021) |
Springer | https://link.springer.com (accessed on 25 May 2021) |
Sage | https://journals.sagepub.com (accessed on 12 May 2021) |
Google Scholar | https://scholar.google.com (accessed on 25 July 2021) |
ACM | https://www.acm.org (accessed on 20 May 2021) |
IEEE | https://ieeexplore.ieee.org (accessed on 28 June 2021) |
Criteria | |
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Inclusion |
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Exclusion |
|
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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|
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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 |
| |||
| ||||
[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. |
|
|
[123] | Resource allocation method with minimum interference for two-hop D2D communications | Interference which reduces network throughput. |
|
|
| ||||
[124] | Multiband cooperative spectrum sensing and resource allocation framework | Energy consumption for spectrum sensing. |
|
|
| ||||
[125] | Channel-time allocation PSO Scheme | To acquire gigabit-per-second throughput and low delay for achieving and maintaining the QoS. |
|
|
|
| |||
[126] | Heterogeneous (high density)/hierarchical (low density) virtualized software-defined cloud RAN (HVSD-CRAN). | Density of users. |
|
|
| ||||
[127] | Mini slot-based slicing allocation problem (MISA-P) model | The probability of forming 5G slices. |
|
|
| ||||
| ||||
[128] | A joint resource allocation and modulation and coding schemes | Requirement of extremely low latency and ultra-reliable communication. |
|
|
|
| |||
[129] | QoS/QoE-aware relay allocation algorithm | Neglects temporal requirements for optimum performances. |
|
|
|
| |||
| ||||
[130] | The learning-based resource allocation scheme | Interference coordination complexity and significant channel state information (CSI) acquisition overhead. |
|
|
| ||||
[131] | Device-to-device multicast (D2MD) scheme | Improving spectrum and energy efficiency and enabling traffic offloading from BSs to device. |
|
|
| ||||
[132] | Constrained deferred acceptance (DA) algorithm and a coalition formation algorithm | The interference management among D2D and current users. |
|
|
|
| |||
[86] | Novel resource allocation schemes (hybrid resource management) | Energy efficiency and consumption. |
|
|
[133] | Orthogonal multiple access (OMA) and relay-assisted transmission schemes. | Jointly optimize the block length and power allocation for reducing error probability. |
|
|
| ||||
[134] | Joint user association and Power Control algorithm | Optimizing power control and user association schemes. |
|
|
[135] | Successive convex approximation (SCA) based alternate search method (ASM) | Raise the total sum rate of users. |
|
|
|
| |||
[136] | An online learning algorithm for resource allocation | Inter-tier interference among RRHS and macro-BSs, and energy efficiency. |
|
|
|
| |||
[137] | Joint resource block (RB) and power allocation scheme | Enhance fairness in data rate among end-users. |
|
|
|
| |||
[138] | Hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) | Achieve the SE-EE tradeoff having minimum rate requirement of each user. |
|
|
|
| |||
[139] | Stackelberg game model | High inter-cell interference (ICI) and less energy efficiency. |
|
|
| ||||
[140] | Virtual code resource allocation (VCRA) approach | Reducing the collision probability. |
|
|
|
| |||
[141] | Deep reinforcement learning -unicast-multicast resource allocation framework (DRL-UMRAF) | High-quality services and achieving green energy savings of base stations. |
|
|
|
| |||
[142] | Deep reinforcement learning-based intelligent Up/Downlink resource allocation | The high dynamic network traffic and unpredicted link-state change. |
|
|
|
| |||
[143] | Joint computation offloading and resource allocation scheme | Complete network information and wireless channel state. |
|
|
| ||||
[144] | Deep neural network-Multi objective Sine Cosine algorithm (DNN-MOSCA) | Achieving better accuracy and reliability. |
|
|
| ||||
[145] | The improved resource allocation algorithm | Improving QoS requirements in MTC. |
|
|
| ||||
[146] | Resource Allocation Algorithm | The interference to 5G cellular users (CUs) related to QoS. |
|
|
|
| |||
[147] | Genetic algorithm- intelligent Latency-Aware Dynamic Resource Allocation Scheme (GI-LARE) | Efficient radio resource management. |
|
|
| ||||
| ||||
[148] | A Low-complexity centralized packet scheduling algorithm | Downlink centralized multi-cell scheduling. |
|
|
|
| |||
[149] | Smart queue management method | QoS of end-to-end real-time traffic. |
|
|
|
| |||
[150] | Proposed Optimal Resource Allocation Algorithm | The optimization problem in mixed-integer nonlinear programming (MINLP). |
|
|
|
| |||
| ||||
[151] | A novel packet delivery mechanism | Issues related to using CoMP for URLLC in C-RAN architecture. |
|
|
|
| |||
[152] | Distributed joint optimization algorithm for user association and power control | Improve total energy efficiency and reduce the inter-cell and intra-cell interference. |
|
|
| ||||
[153] | Pollaczek–Khinchine formula based quadratic optimization (PFQO) | Inaccurate transmission recovery delay of URLLC multi-user services. |
|
|
|
| |||
[154] | An outer approximation algorithm (OAA) | Multiple interferences, imbalanced user traffic load. |
|
|
|
| |||
| ||||
[155] | Joint Power and Subcarrier Allocation | URLLC reliability and network spectral efficiency. |
|
|
|
| |||
| ||||
[156] | Weighted Majority Cooperative Game Theory Based Clustering | Increase interference, improper utilization of resources. |
|
|
|
| |||
[157] | Bee-Ant-CRAN scheme | Design a logical joint mapping among RRHS and User Equipment (UE) and RRHS and BBUS too. |
|
|
| ||||
[158] | Noncooperative game theory-based user-centric resource optimization scheme | Enhance the coverage probability and sum rate. |
|
|
|
|
Metrics | References |
---|---|
Response Time | [119] |
End-To-End Delay | [100,149] |
Delay | [94,103,104,106,115,116,124,125,126,148,153] |
Throughput | [89,93,96,97,98,104,106,107,108,110,115,120,121,122,123,124,125,126,131,137,142,144,150,158] |
Packet Loss | [142,147] |
Latency | [102,103,109,115,121,128,133,147,148,151] |
Overhead | [95,114,122,130] |
Jitter | [96] |
Availability | [93,121] |
Spectral Efficiency | [89,90,92,95,97,105,112,113,117,127,136,137,138,155,156,158] |
Fairness | [89,93,96,98,106,107,113,114,132,137,138,144] |
Outage Ratio | [89,94,145] |
Sum Rate | [90,94,101,103,113,118,135,146,154,158] |
Energy Efficiency | [86,90,97,103,110,111,112,114,118,124,134,136,138,139,140,141,142,143,144,152] |
System Performance | [91,95,99,100,101,105,108,113,117,122,132] |
Low Complexity | [91,95,96,98,99,107,108,110,114,117,130,132] |
Power Allocation | [86,91,92,93,94,95,97,110,111,113,118,119,120,132,134,139,152] |
Reliability | [102,124,128,133,155] |
Time Required for RA | [104] |
Scalability | [108] |
Interference | [100,112,113,119,120,132,146,152,154,156] |
Power Consumption | [126,156] |
Feasibility | [128] |
Energy Consumption | [129,143] |
Domain | References |
---|---|
Fronthaul | [86,89,90,93,94,95,99,101,103,104,105,106,107,109,110,111,113,114,115,117,118,120,123,124,129,133,134,137,138,139,140,142,144,145,146,147,149,150,152,153,154,155,156,158] |
C-RAN | [96,97,109,111,112,118,119,122,126,130,135,143,148,151,157] |
H-CRAN | [97,132,136,143] |
Backhaul | [99,110,111,114,133,147] |
Uplink | [96,97,99,104,105,109,110,111,113,119,120,123,124,129,140,142,143,145,146,149,150,153,154,156,157,158] |
Downlink | [86,89,90,94,95,96,97,99,101,103,104,106,107,109,111,112,114,115,117,118,120,122,126,129,130,132,133,134,135,136,137,138,139,142,143,144,147,148,149,151,152,153,154,155,157] |
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Kamal, M.A.; Raza, H.W.; Alam, M.M.; Su’ud, M.M.; Sajak, A.b.A.B. Resource Allocation Schemes for 5G Network: A Systematic Review. Sensors 2021, 21, 6588. https://doi.org/10.3390/s21196588
Kamal MA, Raza HW, Alam MM, Su’ud MM, Sajak AbAB. Resource Allocation Schemes for 5G Network: A Systematic Review. Sensors. 2021; 21(19):6588. https://doi.org/10.3390/s21196588
Chicago/Turabian StyleKamal, Muhammad Ayoub, Hafiz Wahab Raza, Muhammad Mansoor Alam, Mazliham Mohd Su’ud, and Aznida binti Abu Bakar Sajak. 2021. "Resource Allocation Schemes for 5G Network: A Systematic Review" Sensors 21, no. 19: 6588. https://doi.org/10.3390/s21196588
APA StyleKamal, M. A., Raza, H. W., Alam, M. M., Su’ud, M. M., & Sajak, A. b. A. B. (2021). Resource Allocation Schemes for 5G Network: A Systematic Review. Sensors, 21(19), 6588. https://doi.org/10.3390/s21196588