Encyclopedia
Please note this is an old version of this entry, which may differ significantly from the current revision.
Subjects:
Telecommunications
6G and beyond wireless networks will be utilizing RF (below 6 GHz) mm-Wave, and sub THz frequency bands for user access. These three bands, however, have distinct propagation characteristics and bandwidths. Associating users across these bands using available radio resources while meeting different Quality of Services across slices is a difficult optimization problem. This paper analytically solves this problem and provides meaningful discussion on the tradeoffs of doing so.
- coverage probability
- SINR distribution
- user association
- millimeter wave
1. Introduction
Sixth-generation wireless networks are expected to support a wide range of emerging applications, including augmented reality, tactile Internet, wireless brain interfaces, connected robotic systems, and holographic telepresence. To enable these applications, 6G will require extremely high data rates with low latency and high reliability [1].
As the current sub-6 GHz spectrum is becoming crowded and cannot meet the growing demands, mmW and THz frequency bands have been proposed to provide the needed additional spectrum and higher data rates [2,3]. Broadly, RF includes all frequencies from around 9 kHz to 300 GHz, encompassing both microwaves and lower frequencies. This is the broadest definition. More specifically, RF is often defined as 3 kHz to 30 GHz, with microwaves considered separately from 30 GHz to 300 GHz. For cellular communications (Telecom), “RF bands” commonly refers to frequencies below 6 GHz (aka sub-6 GHz), which includes licensed LTE and 5G bands.
mmW and THz technologies are poised to enable key capabilities that will drive many emerging applications. Specifically, the use of higher frequencies allows for the highly accurate estimation of wireless channel characteristics and device-positioning parameters. This level of precision will underpin the delivery of location-aware services that have become increasingly prevalent. In addition, the massive bandwidths available at mmW and THz frequencies facilitate multi-gigabit data rates and wireless connections with ultra-low latency. Together, the abilities to accurately localize, deliver high data throughput, and minimize latency will be crucial for applications such as augmented/virtual reality, autonomous systems, telemedicine, and industrial automation that require real-time, context-aware wireless connectivity. mmW and THz communications are widely seen as essential technologies for fully realizing these next-generation applications [4,5].
The International Telecommunication Union (ITU) defines mmW frequencies as ranging from 30 GHz to 300 GHz. This is a broad definition encompassing the whole millimeter wavelength range, which offers multi gigabit data rates due to the large available bandwidth. For 5G cellular applications (also defined by the US Federal Communications Commission), mmW is often referred to more narrowly as frequencies between 24 GHz and 100 GHz. This captures the mid-band mmW frequencies being used for 5G. Currently, in Canada, 5G mmW describes radio signals in three frequencies: 26, 28, and 38 GHz. However, mmW signals have high attenuation and cannot penetrate solid materials and walls. This leads to blockage issues and requires LOS or highly directional transmissions [6]. THz bands (0.3 to 10 THz) or wavelengths between 1 mm and 100 µm provide even higher data rates and capacity, enabling terabit wireless applications. For communication applications, some sources define THz as frequencies above 100 GHz, i.e., bordering the upper end of mmWave bands. This definition includes lower THz frequencies. The term “THz gap” is sometimes used to refer specifically to frequencies between 0.3 and 3 THz [7], reflecting the challenges of this mid-band range. Some sources define THz communication beginning at wavelengths shorter than 1 mm, or frequencies above 1 THz. This excludes lower THz frequencies. However, it faces challenges due to high atmospheric and molecular absorption losses, strong signal attenuation, and limited propagation range [8,9].
To compensate for the high propagation losses at THz/mmW frequencies, 6G networks will utilize “ultra-massive MIMO” techniques with large antenna arrays embedded in surfaces, dense arrays of plasma nano-antennas that can be integrated into walls and objects to provide highly directional beamforming gains. The integration of satellite, optical, and molecular communications with intelligent reflecting surfaces will help provide a truly ubiquitous connectivity envisioned for 6G networks [10,11].
Although THz and mmW bands offer much larger bandwidths compared to RF, the impact of interference and noise becomes more critical with the increased bandwidth, and practical implementation challenges arise [9]. Therefore, the greatly expanded spectrum at the mmW and THz frequencies will be key enablers for 6G applications that will require terabit data rates and low latency. However, overcoming challenges like blockage, absorption losses, and limited range will require innovative user association techniques [12].
The seamless integration of high- and low-frequency bands’ cells imposes a number of challenges since different BSs will have different transmission powers, coverage areas and data rate capabilities and need to cater to different types of UEs. Therefore, developing a proper UE-BS association algorithm for diverse 6G networks is a tough task [13]. Such an appropriate algorithm shall not only improve the QoS performance for each UE, but shall also ensure fairness for both UEs and BSs [14].
UE association methods have been widely used in cellular networks to optimize network performance by selecting the best serving cell or frequency band for each UE. Traditional user association strategies that simply maximize the SINR ratio [15] cannot be applied here. This will lead to imbalanced load distributions across frequency bands and cells since the UEs tend to associate with the lowest-frequency (RF) band BSs, which would have better better SINR due to the better propagation characteristics and higher transmit powers.
2. User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
User association methods have been widely used in cellular networks to optimize network performance by selecting the best serving cell or frequency band for each user. Traditional user association schemes utilize parameters like received signal strength (like max-SINR [15]) and channel conditions to associate users with the cell or band that provides the highest SNR or SINR. However, such approaches often lead to load imbalance issues and inefficient resource utilization, potentially causing severe impairments in certain links.
To address these issues, some authors have introduced biased algorithms, such as the CRE algorithm [16,17], which artificially enhances the signal strength of weaker BSs by applying a certain bias factor. While this approach has shown promise in increasing system throughput and capacity, its performance is contingent upon the choice of the bias factor, which poses challenges in determining the optimal value. As a result, the performance of less powerful BSs may be negatively affected.
Another interference avoidance method is resource partitioning, called time domain eICIC [18,19]. eICIC uses the muting/coordination of resources, where it coordinates resource allocation between overlapping macro and small cells. Macro cells can mute some subframes, allowing small cells to reuse those resources. This reduces interference from macrocells to small cell users without changing the TX power levels. While eICIC is effective for same-RAT HetNets, applying its interference coordination approach across fundamentally different radio technologies with varying capabilities will be very complex.
Even incorporating blockage probability [22] instead of raw SINR may still favor the RF band since its blockage probability of RF is effectively zero. Normalization or other techniques are needed to overcome this bias.
More recent work has explored optimization-based user association methods that aim to maximize network-wide utility or fairness metrics while balancing the load across cells and frequency bands [23,24,25]. Such schemes incorporate factors like network congestion, user rates, cell capacities, load variances, cell loads, bandwidths, and handover costs into the optimization objective, which can enable load balancing across the network to improve service rates for all UEs. For example, some studies [26,27,28,29] have formulated user association as a mixed-integer program that jointly optimizes user–cell associations and resource allocation to maximize the sum rate of the network. However, these algorithms often result in complex integer programs that are difficult to solve in real time.
Other works have used game-theoretic approaches [30,31], where each user associates with the cell or band that maximizes its own utility while considering the impact on other users. Game theoretic approaches require iterative signaling between users and the network, increasing the signaling overhead. They also assume rational users who would compromise their own utility.
Machine learning methods such as reinforcement learning [32,33,34] have also been explored to dynamically associate users based on real-time network conditions. These self-organizing techniques can address issues like non-convexity and complexity that arise in optimization formulations. However, reinforcement learning approaches generally require large amounts of data and high-dimensional state and action spaces, and may suffer from poor reproducibility and explainability.
In summary, existing algorithms either do not provide fair load distribution, skew results towards RF bands due to higher intrinsic SINR/lower blockage probability, or have issues with high complexity, signaling overhead or data requirements as shown in Table 1. A low-complexity, efficient algorithm is needed to avoid these limitations.
Table 1. Summary of previous user association methods.
| Method | Year | Key Advantages | Key Limitations | Suitability for mmW/THz |
|---|---|---|---|---|
| SINR-based | 2012 | Simple implementation; associates users to BS with strongest signal | Does not consider load balancing or multi-band characteristics | Not suitable due to variability in bands |
| Biased algorithms | 2015–2017 | Increased capacity and throughput | Optimal bias factor difficult to determine | Marginally improves performance |
| eICIC | 2013–2017 | Reduces interference in HetNets | Complex to apply across technologies | Complexity limits applicability |
| Load-aware | 2019–2020 | Balances SINR and load | Favors RF band due to higher intrinsic SINR | Better than SINR but still biased |
| Blockage-aware | 2021 | Considers propagation effects | Improved but bias toward RF remains | |
| Optimization | 2018–2023 | Maximizes network utility | Complex formulations, high complexity | High complexity limits real-time use |
| Game theory | 2020–2021 | Models user self-interest | High signaling overhead, assumptions | Signaling overhead challenging |
| Reinforcement learning | 2017–2021 | Adapts dynamically | High computational cost; requires extensive training; poor explainability | Applicable but data/complexity concerns |
This entry is adapted from the peer-reviewed paper 10.3390/fi15120376
This entry is offline, you can click here to edit this entry!
