# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 2 Related Work  
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. Some works consider both the SINR and existing load (like our previous algorithms [20; 21]. While this balances the SINR and load distribution better than max-SINR, this still considers absolute SINR values without normalization across bands. This would skew results towards the RF band. 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.