Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks
Abstract
:1. Introduction
2. Contribution
3. Spectrum Management Issues
3.1. A. Carrier Aggregation
3.1.1. Resource Sharing
3.1.2. Energy Efficiency
3.1.3. Capacity Improvement
3.1.4. Transmission Performance
3.2. Cognitive Radio
3.2.1. Spectrum Sensing
3.2.2. Throughput Enhancement
3.2.3. Spectrum Allocation
3.2.4. Channel Estimation and Optimization
3.2.5. Cluster Formation
3.3. Small Cell
3.3.1. Interference Avoidance
3.3.2. Throughput Improvement
3.3.3. Coverage Planning
3.3.4. Capacity Enhancement
3.4. High-Spectrum Access
3.4.1. Below 6 GHz (Sub-6 GHz)
3.4.2. NR mmWave (24–100 GHz)
3.4.3. Above 100 GHz (100–300 GHz)
3.4.4. Outdoor Investigation
3.4.5. Indoor Investigation
3.5. M-MIMO
3.5.1. Minimizing BER
3.5.2. Spectrum Sensing
3.5.3. Receivers Design
3.5.4. Channel Modeling
4. Future Research Challenges
4.1. Carrier Aggregation
4.2. Cognitive Radio
4.3. Small Cell
4.4. High-Spectrum Access
4.5. M-MIMO
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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---|---|---|---|---|
Resource sharing | Component Carrier (CC) selection based on the head of the line delay and threshold delay | Increase network throughput and reduce computational complexity | No improvement in fairness index | [91] |
Design an efficient packet scheduling algorithm based on proportional fairness to use in multiple CC’s systems | Support both real and non-real-time traffic | Inefficient when packet traffic is fluctuating | [92] | |
Joint optimization technique based on a greedy-based algorithm for CC selection | Computational complexity is decreased | Low fairness index for cell-edge users | [93] | |
Traffic and channel-driven CC selection by considering channel quality and traffic load | Better performance as compared to least-load and max channel quality indicator (CQI) algorithm | Low fairness index when a high number of users in a cell | [94] | |
Energy efficiency | Relaying scheme to improve the coverage, fairness, and capacity for CA-based system | Work for both intra-and inter-band CA | More advance algorithm is needed for the mobility of users | [95] |
The Bisection Method for Energy Minimization (BIMEM) algorithm is used to minimize the energy consumption and capacity maximization | Reducing network capacity and improves massive energy saving | Interference effect due to multiple BSs on the same layer | [96] | |
Capacity improvement | User scheduling and combined cell association, where the user can connect BSs by using multiple carrier bands | Convex optimization solutions to enhance the network capacity | Computational complexity increases for high users | [97] |
Cross-layer scheduling approach based on three mechanisms: (1) Markov Decision Process-Based Cost Reward Packet Selection (MDP-PS), (2) Adaptive Packet Scheduling (APS), and (3) Adaptive Component Carrier Scheduling (ACC) | Better results for capacity, network reward, and packet failure rate | An analytical method is needed for centralized radio access network (C-RAN) and energy-efficient cognitive radio (CR) | [98] | |
Transmission performance | Receiver design architecture based on cascade-shutoff Low-Noise Transconductance Amplifier (LNTA) | Support both inter-band and intra-band | Limited to Single-Input-Multiple-Output (SIMO) scenario only | [99] |
A latency-efficient Code-Division Multiplexing (CDM) CA approach based on least-squares approximation | Reduce the number of iteration and latency | Limited transmission distance of maximum 10 km | [100] |
Approach | Methodology/Technique | Advantages | Limitation/Future Work | References |
---|---|---|---|---|
Spectrum sensing | A new Filter Bank Multicarrier (FBMC) approach based on the adjustment by using the non-linear fractional program and stationery KKT condition | Efficient utilization of network resources for real-time Internet of Things (IoT) applications | Bio-inspired techniques for more efficient optimization approach | [116] |
A group-based multi-channel synchronized spectrum sensing approach based on Dynamic Multi-Channel Slot Allocation (DMCSA) algorithm | Optimal performance in terms of throughput, detection probability, delay, and sensing overhead | Limited to a smaller number of users | [117] | |
Throughput enhancement | It derives an optimal service rate for increasing the performance of primary and secondary users | Better Quality of Service (QoS) and throughput performance with minimum sojourn time | Queue length factor is not considered | [118] |
The undercover routing protocol, which consists of collaborative beamforming technique based on layer three routing | Gain increased up to 250% as compared to conventional protocols | Group construction time needs to be improved | [119] | |
Spectrum allocation | The Chaotic Biogeography-Based Optimization (CBBO) algorithm to solve combinational optimization problems | CBBO performance is higher as compared to other traditional algorithms | Non-linear migration model can be used in future | [120] |
Channel Management Framework (CMF) is introduced which is based on opportunity detector, scheduler, and ranker | Improvement in a collision, blocking, detection, and idle-time probability | Mobility factor is not considered in this scenario | [121] | |
Channel estimation and optimization | The Second- And Fourth-Order Moments (M2M4) method is introduced to calculate real-time Signal to Noise Ratio (SNR) value | Gives accurate and reliable channel state information | The prediction error is high as 0.14 dB | [122] |
An alternative optimization framework to enhance the variables of subcarrier assignment and power allocation | Better energy efficiency as compared to the conventional resource allocation scheme | Limited to one user per cell only | [123] | |
Cluster formation | Localized clustering technique, which shares weight to neighboring nodes to solve the mobility issue | Improves stability, scalability, and efficient spectrum management with low overhead delay | Multi-layer complex algorithm | [124] |
The cluster-based scheduling approach is proposed, namely, Frame- Intra Cluster Multichannel Scheduling algorithm (ICMS) and Slot-ICMS. | Enable spatial reuse along with non-interfering users | The overhead delay increases | [125] |
Approach | Methodology/Technique | Advantages | Limitation/Future Work | References |
---|---|---|---|---|
Interference avoidance | A non-orthogonal interference-free Spectrum Sharing (SS) approach to form 3D clusters and less distance among co-channel small cells | Better results as compared to the orthogonal spectrum for both Licensed Shared Access (LSA) and Licensed Assisted Access (LAA) method | The results for larger small cell size need to be investigated for the validity of the proposed approach | [141] |
It utilizes an Almost Blank Subframe (ABS) scheme to analyze the operation effect between various small cells | Proposed network architecture delivers cost-effective and interference avoidance results | Limited transmission range | [142] | |
Throughput improvement | Optimal memory size is calculated based on the user’s requesting probability | The optimal size memory delivers better throughput performance | It reduces the backhaul capacity | [143] |
A cooperative game theory-based RRM scheme for small cell network | Results for user throughput and spectral efficiency are better as compared to no game scenario | Distributed learning approach can be applied for more efficient coalition formation | [144] | |
Coverage planning | Spectral efficiency and capacity improvement technique for isolated mmWave MU-MIMO small cell users | High throughput is achieved when each cell sector operates in three channels | Advanced interference mitigation techniques are required | [145] |
Mitigating the coverage hole issue for a two-layer small network | Better coverage, power usage, and transmission rate | A more efficient algorithm is required to support higher-layer network | [146] | |
Scheduling technique based on BS density adaptation algorithm and a cell-zooming algorithm | Better coverage, throughput, and spectral efficiency | Can enhance work for the mobility of BS | [147] | |
Capacity enhancement | To enhance the number of traffic demand nodes based on power limited, bandwidth, and traffic requirement | Higher network capacity with low deployment cost | Relay node can be added to enhance the coverage area | [148] |
The file cloud service is used to offload the mobile user data when the user’s data demand increases | Higher accessibility increases the coverage area | Delay increases with the greater file size | [149] | |
Design of a self-organizing Artificial Immune System (AIS) approach that activates and deactivates small cells concerning the traffic load | Helps to increase the coverage and cell-edge user’s throughput | Activation and deactivation process is affected by interference | [150] |
Issues/Approach | Methodology/Technique | Advantages | Limitation/Future Work | References |
---|---|---|---|---|
Outdoor investigation | Channel characterization performed at 26 GHz for tropical outdoor parking environment by utilizing Close-In Free Space (CI) and Floating Intercept (FI) path loss model | The CI model performance is higher than the FI model | More efficient model is needed for mobile users | [190] |
MIMO channel characterization at 15 GHz for the outdoor scenario by using Okumura Hata and microcell model | Suggested 15 GHz band has high data, which is suitable for future network | Limited bandwidth | [191] | |
Propagation characteristics for 28 and 73 GHz for the outdoor environment by using CI and FI propagation model | Higher frequency spectrum delivers more data rate | More accurate path loss model, such as Close-In Frequency Weighting (CIF) and Alpha-Beta-Gamma (ABG), can be used | [192] | |
Propagation characteristics comparisons of 28 and 73 GHz for the outdoor scenario using the ABG path loss model | Better throughput and spectrum efficiency | Signal degrades when interference increases | [193] | |
Path loss based on directional and omnidirectional antennas for 32 GHz for both Line of Sight (LOS) and Non-Line of Sight (NLOS) outdoor environment | CI model results are suitable than the FI model for NLOS scenario | A high directional antenna is needed | [194] | |
Channel characteristics for 28, 38, 60, and 73 GHz outdoor scenarios using a wideband sliding correlator channel sounder with the horn-to horn antenna configuration | Accurate values of path loss exponent (PLE) is achieved | Precise horn antenna alignment is needed | [195] | |
Indoor investigation | Channel behavior in an indoor environment for 28 and 73 GHz using the CI model | Offer simplicity and better network performance | 73 GHz signal suffers from more scattering issue | [196] |
Polarization effect estimation for indoor LOS environment at 60 GHz using ray-tracing simulation | Performance error is identified | Can enhance the throughput with 73 GHz | [197] | |
Indoor office environment investigation at 28 and 73 GHz using horn and omnidirectional antenna in co- and cross-polarization antenna settings for both LOS and NLOS environment | Calculate delay spread values and determine the factor of time delay | Precise alignment is required for LOS scenario | [198] | |
Indoor effect of material’s conductivity and permittivity at 28, 39, 60, and 73 GHz for LOS and NLOS using 3D ray-tracing wireless insite software | Amount of received power and delay spread decreases along with the frequency | More efficient power optimization scheme is needed | [199] |
Issues/Approach | Methodology/Technique | Advantages | Limitations/future Work | References |
---|---|---|---|---|
Minimizing Better Bit Error Rate (BER) | MATLAB simulations of approximate message passing algorithm for uplink detection | Efficient and less complicated uplink detection and the excellent tradeoff between complexity and performance | Can be extended to large M-MIMO systems with a vast number of antenna and users | [227] |
Training-based blind channel estimation techniques | BER count | Complex algorithm | [228] | |
Spectrum Sensing | Direct localization algorithm, which is based on the location to source for narrowband multipath | Decreases execution time and enhances the spectrum accuracy | Higher computational complexity | [229] |
Performance analysis of spectral efficiency and BS antennas using match filter pre-coding techniques | Improves throughput and spectral efficiency | More channel information is needed for the pilot signal | [230] | |
Receiver design | Multi-user MIMO precoding schemes, i.e., Eigen Zero Forcing (EZF), Tomlinson-Harashima Precoding (THP), for different UE deployment scenarios | Flexibility in a practical system design | Limited to LOS environment only | [231] |
TDD realization based on Zero Forcing (ZF) and Maximum Ratio Combining (MRC) schemes for uplink M-MIMO system | Spectral efficiency has a significant improvement, and the design condition is dependent on the number of antennas on the BS and pilot reuse factor | Limited to a smaller number of antennas | [232] | |
Channel modeling | Real measurement has been performed at 2.6 GHz by using the virtual Uniform Linear Array (ULA) and Uniform Cylindrical Array (UCA) | Better performance close to that in i.i.d. Rayleigh channels | More transmission factors, such as propagation delay, should be included in future | [233] |
Utilizes first-order Gauss–Markov Rayleigh fading channel model in time-selective channels | Optimum results in the achieved aggregate-rate | Interference effect is not considered | [234] | |
It has designed an M-MIMO correlated channel by using MATLAB simulations to solve pilot contamination issue | Achieves better performance by increasing more antennas at Base Station (BS) | Correlated channels reduce the overall performance | [235] | |
A scheduling algorithm based on the downlink M-MIMO system along with Zero Forcing (ZF) beamforming approach | Better results in terms of error performance, sum rate, throughput, and fairness | Need to test on the more realistic model and for multi-antenna users | [236] |
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Qamar, F.; Siddiqui, M.U.A.; Hindia, M.N.; Hassan, R.; Nguyen, Q.N. Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks. Electronics 2020, 9, 1416. https://doi.org/10.3390/electronics9091416
Qamar F, Siddiqui MUA, Hindia MN, Hassan R, Nguyen QN. Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks. Electronics. 2020; 9(9):1416. https://doi.org/10.3390/electronics9091416
Chicago/Turabian StyleQamar, Faizan, Maraj Uddin Ahmed Siddiqui, MHD Nour Hindia, Rosilah Hassan, and Quang Ngoc Nguyen. 2020. "Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks" Electronics 9, no. 9: 1416. https://doi.org/10.3390/electronics9091416