Evolution of Wireless Communication to 6G: Potential Applications and Research Directions
<p>Comparison of Key Performance requirements between 5G and 6G [<a href="#B7-sustainability-14-06356" class="html-bibr">7</a>].</p> "> Figure 2
<p>Comparison of 6G features with respect to 5G [<a href="#B36-sustainability-14-06356" class="html-bibr">36</a>].</p> "> Figure 3
<p>Structure of study.</p> "> Figure 4
<p>6G Visions [<a href="#B48-sustainability-14-06356" class="html-bibr">48</a>].</p> "> Figure 5
<p>The 6G patent applications by country/region (As of August 2021, in percent ) [<a href="#B51-sustainability-14-06356" class="html-bibr">51</a>].</p> "> Figure 6
<p>Role of blockchain for 6G networks [<a href="#B62-sustainability-14-06356" class="html-bibr">62</a>].</p> "> Figure 7
<p>Quantum Communication (QC) derived from [<a href="#B9-sustainability-14-06356" class="html-bibr">9</a>].</p> ">
Abstract
:1. Introduction
1.1. Paper Motivation
1.2. Paper Contribution
2. Evolution of Mobile Network Architecture
2.1. Core Network (CN) in 5G
2.2. Ran Evolution of Mobile Networks
2.3. Multi-Access Edge Computing (MEC)
2.4. Open-Radio Access Network (O-RAN)
2.5. Network Slicing
2.6. xHaul Architecture
3. Post 2030 Prospective 6G Application Areas
3.1. Experiencing the Personal Edge Intelligence
3.2. Wireless Communication for Automotive Sector
3.3. Internet of Things (IoT) Supported Smart City Services
3.4. Autonomous Ports and Autonomous Manufacturing
3.5. Bio-Cybernetic Based Identity
4. Statistics of the Patents Dedicated to 6G
5. Future 6G Enabling Technologies
5.1. Big data and Deep Learning
5.2. Quantum Communications
6. Key Driving Technologies for 6G
6.1. VLC and terahertz Regime
6.2. THz Challenges
6.3. Molecular Communication
6.4. Bio-Signal Processing
6.5. Blockchain
6.6. Energy Harvesting for Mobile Charging
- (i)
- The energy obtained from an “ambient environment” can be utilized as a potential substitute of energy supply. This can also meet the energy requirements of radio access network(s) (RAN).
- (ii)
- Moreover, radio frequency (RF) interference, “RF interference energy harvesting” has also been considered an option where the “ultra-dense” networks having numerous nodes can potentially produce interference energy.
- (iii)
- The energy produced by “the artificial Jamming and noise” is also considered a resource for energy supply [70].
7. Major Challenges and Research Areas for 6G
7.1. Data Security and User Privacy Challenges
Privacy-Enhancing Computation (PEC)
7.2. Hybrid Radio Networks (RN) and Visible Light Communication (VLC)
7.3. Wireless Networking for Cyber-Physical Systems (CPS)
7.4. Quantum-Based Wireless Systems Design
7.5. Multiple Access and Modulation Techniques
- (i)
- Frequency Division Multiple Access (FDMA);
- (ii)
- Time Division Multiple Access (TDMA);
- (iii)
- Code Division Multiple Access (CDMA); and
- (iv)
- Orthogonal Frequency Division Multiple Access (OFDM).
- (i)
- “frequency selective fading”;
- (ii)
- “co-channel interference”; and
- (iii)
- “impulse noise”.
7.6. Processing of Sensor Data at High Speed
7.7. Satellite Communication
7.8. RF Exposure and Related Human Health Concerns
7.9. Electromagnetic Compatibility
7.10. Reshaping the Developing Economies, Last Mile
7.11. Ethical Responsibilities and User Awareness Programs
8. Machine Learning Technologies as Potential Enablers of 6G Era
- (i)
- “Very high data rates, up to 1 Tbps”;
- (ii)
- “Very high energy efficiency capable of supporting ‘battery-free IoT devices”;
- (iii)
- “Reliable global/terrestrial connectivity”;
- (iv)
- “Massive low-latency control (less than 1 msec E2E latency)”;
- (v)
- “Very broad frequency bands (e.g., 73 GHz–140 GHz and 1 THz–3 THz)”;
- (vi)
- “Ubiquitous always-on broadband global network coverage”;
- (vii)
- “Connected intelligence powered by machine learning”.
8.1. Machine Learning Approaches
8.2. ML/Dl Driven Solutions
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
MDPI | Multidisciplinary Digital Publishing Institute |
DOAJ | directory of open access journals |
TLA | three letter acronym |
LD | linear dichroism |
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Author (s) | Contribution |
---|---|
[5] | mmW millimeter-wave enabling technologies. |
[6] | Developments toward 6G |
[8] | Sixth generation (6G) wireless system and role of ML techniques. |
[9] | Sixth generation (6G) drivers, use cases, usage scenarios, requirements, KPIs, architecture, and enabling technologies. |
[10] | Energy, IoT, and ML in 6G. |
[11] | Digital twins for wireless systems |
[12] | Quantum search algorithms for wireless communications |
[13] | Advancements in a DL-based physical layer (PHY) of 6G. |
[14] | Wireless evolution toward 6G networks and related potential technologies. |
[15] | Optimization frameworks and performance analysis methods for large intelligent surfaces (LIS). |
[16] | Scalable and trustworthy edge AI systems. |
[17] | Technology transformations to define 6G. |
Characteristics | 5G Advanced | 6G |
---|---|---|
Peak data rates | 100 Gbps | 1 Tbps |
Latency | 1 ms | <1 ms |
Frequency bands | Sub 6 GHz, mmWave for fixed access | Sub 6GHz mmWave for mobile access, terahertz band, Non-RF e.g., VLC) |
Device Services | Secure connectivity | Physical interaction in real-time scenarios |
Network Type | SDN, NFV, Slicing | SDN, NFV, Intelligent Cloud, AI-based Slicing, Deep learning |
Computing Techniques | Fog computing, Cloud computing | Quantum computing, Edge computing |
Mobility | 500 Km/h | >700 Km/h |
Technology | D2D communication, Ultra-dense Network, Relaying, Small cell access, NOMA | Visible Llight Communication, Quantum Communication, Hybrid access, Haptic technology |
Application types | Reliable eMMB, URLLC, mMTC, Hybrid | MBRLLC, mURLLC, HCS, MPS |
Architecture | Dense sub-6 GHz small cells with | Cell-free smart surfaces, Temporary hotspots using drones base stations |
Author (s) | Contribution |
---|---|
[33] | Reviewed xHaul architecture related standards and activities. |
[37] | Reviewed “TSN-aware xHaul network” |
[46] | Presented 5G-xHaul architecture features. |
[47] | Presented implementation of a “multiband and photonically amplified fiber-wireless (FiWi)” xHaul. |
Parameters | Frequency Dependence | Effects on 6G THz | THz vs. Microwave and FSO |
---|---|---|---|
Spreading Loss | Quadratic fluctuation with area and “frequency-dependent gains” | Distance Limitation | >microwave, <FSO |
Atmospheric Loss | Frequency-dependent path loss peaks | frequency-dependent spectral windows with varying bandwidth | No perceptible impact on microwave frequencies, oxygen molecules at millimeter wave, water, and oxygen molecules at THz, water, and carbon dioxide molecules at FSO |
Diffuse Scattering and Specular reflection | “Scattering increases” with frequency. Frequency-dependent reflection loss | Limited multi-path sparsity | Stronger than microwave, weaker than FSO |
LoS prob, Diffraction, and Shadowing | Negligible diffraction, shadowing and penetration more losses with frequency increase. Frequency-independent loS probability | Low multi-path high sparsity and dense spatial reuse | more than microwave less than FSO |
Weather Influences | Frequency-dependent airborne particulates scattering | Potential constraints in THz outdoor communications with heavy rain attenuation | >than microwave, <FSO |
Scintillation Effect | Increase with frequency | Constraint in THz space communications | No clear effects at microwave, THz is less susceptible than FSO |
No | Author (s) | Contributions |
---|---|---|
1 | [34] | Presented comparison of “blockchain-based spectrum management” and legacy “centralized approach”. |
2 | [57] | Proposed a novel consensus E-PoW, where MMC in AI training is integrated into the block mining process. |
3 | [58] | Presented a distributed watchdogs based on blockchain for securing IIoT |
4 | [59] | Propose a ‘BlockEdge’ (blockchain edge) framework that combines these two enabling technologies to address some of the critical issues faced by the current IIoT networks. |
5 | [60] | Reviewed blockchain and ML for IoT in 5G and beyond networks. |
6 | [62] | Proposed an “IoT and blockchain-enabled” optimized provenance system for Industry 4.0. |
7 | [63] | Presented an overview of blockchain as potential security solutions “edge computing”. |
8 | [64] | Overviewed the blockchain technologies and protocols that can be used to implement blockchain-backed network asset trading over 5G and Beyond networks. |
9 | [65] | Examined the security problems with blockchain-enabled IoT with a 6G communication network. |
10 | [66] | Designed a new digital twin wireless network model—DTWN. |
S. No | Centralized Spectrum Management Issues | Benefits of Blockchain-Based Spectrum Management |
---|---|---|
01 | Security risks | Distributed storage and encryption methods/algorithms are used for the security of the users’ data |
02 | No incentive approach for spectrum sharing | The incentive mechanism (e.g., virtual currency) can attract nodes for spectrum sharing |
03 | Exposed to the threat of malicious attacks | Validation mechanisms offer security for the transactions |
04 | Low spectrum allocation efficiency | Distributed spectrum management offers effective and improved spectrum allocation |
05 | Expensive maintenance | Cost effective for maintenance as there is no centralized database to be maintained |
No | Parameter | Description | Potential Benefits in AR/VR Applications |
---|---|---|---|
1 | Decentralization | Decentralization of record maintenance eliminating the single-point risk of failure. | Blockchain offers the decentralization of different communication devices. |
2 | Tokenization | Facilitation users to exchange values on different networks. | Blockchain improves financial transactions using digital tokens. |
3 | Immutability | Consensus mechanism enables data storage in distributed ledger form, which provides unalterable and tamper-proof record maintenance. Consensus mechanism also provides the integrity of the end-to-end (E2E) system | Blockchain provides an improved way of sharing/exchanges of audio/video data between server and device in a secure way. |
4 | Scalability | Scalability provides increased transaction load and number of nodes in the network. | Blockchain provides an increase in the transaction size for all blocks for storing and peering data using an interplanetary file system. |
5 | Anonymity | provides trust mechanism among unknown nodes in network | Blockchain enables nodes create unique digital assets that cannot be copied. |
6 | Security | Offers security for possible attack against data by encrypting the data through cryptographic algorithms having no relationship for private and public keys. | Blockchain can offer improved scalability for AR/VR in tactical applications. |
Author (s) | Contribution |
---|---|
[9] | Presents attacks in optical networks and a solution through “quantum- secured blockchain”. |
[46] | Presented a scheduling algorithm for mmWave observation. |
[74] | Studied the problem of optimal distribution of entanglement generation for multiple heterogeneous users in a quantum communication network. |
[75] | Design and security analysis of the QKD protocol over FSO |
[76] | Presents “Quantum Search Algorithms for Wireless Communications |
[12] | Proposed “Quantum Learning Based Nonrandom Superimposed Coding for Secure Wireless Access in 5G URLLC” |
[77] | Studied Quantum security authentication and key management in LTE. |
[78] | Developed an integrated polarization beam splitter (PBS) module with silica planar lightwave circuit technology for use in QKD. |
No | Author (s) | Contribution | Type |
---|---|---|---|
1 | [8] | Proposed a federated learning (DFL) framework for autonomous driving cars. | FL |
2 | [21] | DL-based “radar-aided beam prediction” approaches for mmWave/sub-THz. | DL |
3 | [48] | Proposed a graph attention Q-learning (GAQ) algorithm for tilt optimization | RL |
4 | [72] | Presented FL in LEO-based networks. Reviewed LEO-based SatCom ML technique networks. | FL |
5 | [85] | Proposed an AI0based “digital twin enabled network framework” for 6G networks. | DL |
6 | [87] | Investigated the positioning for autonomous vehicles. | RL |
7 | [86] | Proposed a learning-driven detection scheme using lightweight CNN. | DL |
8 | [88] | A ML approach for intermodulation interference detection in 6G | ML |
9 | [89] | Computer vision-aided beam tracking in millimeter wave deployment | DL |
10 | [90] | Proposed to use a cell-free massive MIMO to ensure stable operation. | FL |
11 | [91] | Designed a DL-based LR channel estimation method—MIMO channel V2X. | DL |
12 | [92] | DL-based load balancing for QoS in 6G | DL |
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Asghar, M.Z.; Memon, S.A.; Hämäläinen, J. Evolution of Wireless Communication to 6G: Potential Applications and Research Directions. Sustainability 2022, 14, 6356. https://doi.org/10.3390/su14106356
Asghar MZ, Memon SA, Hämäläinen J. Evolution of Wireless Communication to 6G: Potential Applications and Research Directions. Sustainability. 2022; 14(10):6356. https://doi.org/10.3390/su14106356
Chicago/Turabian StyleAsghar, Muhammad Zeeshan, Shafique Ahmed Memon, and Jyri Hämäläinen. 2022. "Evolution of Wireless Communication to 6G: Potential Applications and Research Directions" Sustainability 14, no. 10: 6356. https://doi.org/10.3390/su14106356
APA StyleAsghar, M. Z., Memon, S. A., & Hämäläinen, J. (2022). Evolution of Wireless Communication to 6G: Potential Applications and Research Directions. Sustainability, 14(10), 6356. https://doi.org/10.3390/su14106356