The OHMDLNN handoff decision model is assessed within a 5G heterogeneous cellular network environment. The system considers different base stations and mobile users possessing diverse mobility patterns, in a dense user scenario, where handoffs are expected to happen frequently. Furthermore, the UE is modelled to navigate along the cell boundaries, creating dynamic handover events. The mobility management (MM) problem is defined as a multi-class classification task, where the purpose is to achieve the optimum HO decision at each timestamp. This HO decision is made by considering both the system-level indicators and the radio link quality parameters. By considering both types of parameters, the model will be able to capture the temporal dependencies as well as optimize HO success while decreasing packet loss, signalling overhead, and latency. The algorithmic workflow of the proposed model is summarized as follows:

The Algorithm 1 provides a high-level description of the operational flow of the proposed model in a high level, as it demonstrates how the proposed model supports the generation of intelligent and reliable handover decisions. Through a systematic combination of important inputs such as QoS parameters, mobility context and radio measurements. The algorithm is implemented to maximize the network performance to reduce the loss of packets, the delay of handover, and the siganling overhead. Such algorithmic expression forms the basis for the training of the model and the following performance evaluation, which is presented in the subsequent sections.

To ensure realistic simulation, key 5G parameters such as handover thresholds, mobility statistics, and path loss characteristics are derived from the DeepSlice dataset [29]. First, the Deep Slice data is used to train the model. However, operational 5G working information was not available during testing. We thus created a simulated 5G environment to create a synthetic data to test our model. As such, the experiment workflow will consist of the following steps: training on the DeepSlice dataset, testing and evaluation on the simulated 5G dataset. This dataset was chosen because it for its relevance and appropriateness to the problem domain of the article. The HO indicators that are employed in this dataset consist of RSSI, RSRQ, RSRP and channel quality indicator (CQI) which are significant measures of quality and strength of any channel. Earlier deep learning models have also utilized these parameters to control HO in 5G and cellular networks [30]. In the same way, the user count, QoS flow identification, and other values are also included in the dataset that provide detailed information about the network conditions and user requirements. Every record in the dataset describes the state of the network at the time of a HO event. Both network-side and user-side features are unified in the dataset to ensure the comprehensive representation of the MM environment. The dataset indicators (input parameters) can be categorized as follows:

Signal-based include RSSI, RSRP, and RSRQ.

The above set of parameters provides a balanced representation of user experience and network performance. Enabling the model to learn effective decision-making under diverse conditions. Further details of the input parameters in the dataset, along with their corresponding values, are presented in Table 2.

The architecture of the proposed model employs multiple libraries and processing tools. The NumPy and Pandas packages are selected as preferred tools due to their efficient integration of data-handling capabilities with numerical operations. They also provide strong support for data organisation and evaluation functions [31]. Additionally, matplotlib and seaborn libraries are employed to generate graphical representations of data visualization. The input data preprocessing procedures are performed in the following manner:

Normalization: This is done by using LabelEncoder and StandardScaler of scikit-learn library, to standardize the input features and encode them accordingly.

In addition, the model used stochastic gradient descent (SGD) as well as the Adam optimization algorithm. Similarly, the dataframe of pandas was used to handle and manage the CSV file datasets. The data quality assessment employed two assessment methods: unique value quantity assessment and missing value analysis.

The model performs classification by processing distinct groups of information through its structured layers. These layers include an input layer, a sequence of hidden layers (which consist of a TCN layer, a dropout layer, and a flatten layer), and the output layer, as shown in Fig. 2. The specific functions of each layer are as follows:

The proposed model in mathematical form can be represented as follows: (1)In=PRF+PNDB

In Eq. (1),

In represents the input.

PRF represents the input parameters related to radio frequencies.

PNDB represents the database-related information. (2)out=h(l)(HL2nX64{EL(In)})

The output of OHMDLNN is computed using Eq. (2), where E_L denotes the embedding layer. Similarly, h(l) represents the activation vector of the l-th hidden layer. The dropout operation is defined by the following Eq. (3): (3)hdropout(l)=11−p⋅m⊙h(l)where, p=0.5 is the dropout probability, m∈{0,1}n is a Bernoulli mask, ⊙ represents element-wise multiplication, and the scaling factor 11−p conserves the predictable activation magnitude during training. Likewise, the notation 2ⁿ × 64 is a configuration descriptor for the TCN’s dilated convolution block on H_L. The real operation for a 1D input sequence x with filter f of size k and dilation factor d = 2ⁿ is defined by the following Eq. (4): (4)(x∗df)(t)=∑i=0k−1f(i)⋅x(t−d⋅i)here, n is the block index and 64 represents the number of channels per layer. As a result, this exponential dilation enables the model to capture long-term temporal dependencies without a proportional increase in parameters. Lastly, in Eq. (2), H_L identifies hidden layer and then the output of OHMDLNN is computed by applying E_L on input I_n followed by successive hidden and dropout layers. (5)HL=Tanh(ReLU(W∗X+bo))+X

In Eq. (5),

H_L represents the output of the hidden layer, after dropout regularization.

ReLU(HL) represents rectified linear unit activation. Which introduces nonlinearity to the model.

W₀ represents the weight matrix of the output layer.

b₀ is the bias factor for the output layer.

Tanh is used as the hyperbolic tangent activation function; it normalizes the output and represents both positive and negative values.

Finally, X denotes the residual connection that facilitates gradient flow through the deeper layers of the network (6)Fout=σ(HL)

The final output of the model is computed by applying the softmax operation to HL, as shown in Eq. (6), where σ denotes the softmax function. This operation guarantees the consistency of output, and it avoids scores from being excessively small or large as in the case of unnormalized values.

The model is trained on a dataset of 65,000 records. To ensure rigorous development and assessment of the model, the data is split into three separate subsets: training, validation, and testing with the ratios of 70, 15, and 15 percent, respectively. This data splitting adheres to a conventional hold-out validation approach, which is necessary for achieving an unbiased assessment of the model. The model training is executed under the following settings:

Optimizer: SGD optimizer with momentum.

Validation Strategy

The validation set is a key part of model development. It is applied in hyperparameter tuning, where a grid search is performed to optimize important hyperparameters, and the model is chosen according to its results. Validation set performance metrics include accuracy and loss monitored every epoch. This is a good predictor of how the model would behave on unknown data. They direct early termination of the process and help to avoid overfitting during the weight update process. Once the training and hyperparameter optimization have been completed, the model is tested on the held-out test set. This test provides a final and objective estimate of how well the proposed model can generalize to new, unknown data.

The proposed model can be utilized in different scenarios of intelligent handover management. It is most effectively used to provide dependable communication with low latency [33]. In enhanced mobile broadband scenarios, it helps to support seamless connectivity for high-speed users by minimizing packet loss and enhancing the handover success ratio [11]. In the case of low-latency, high-reliability, such as remote sensing and self-driving cars, it provides reliable and continuous data transmission. Moreover, in the context of heterogeneous networks with high population density, the model can be used to make smart handover decisions based on network-level KPIs, leading to improvements in overall quality of service and network stability. The combination of these applications demonstrates the usefulness and feasibility of the proposed model in real 5G networks.

The exploration and evaluation of the OHMDLNN model were conducted to ensure its reliability and generalisability within a well-established 5G simulation environment. Simu5G, a simulator based on OMNeT++ [34] enables realistic deployment of 5G NR and supports mobility management functionalities. It provides accurate modelling of base stations, user equipment behaviour, and radio channel conditions in both urban and suburban settings. Table 3 presents the simulation parameters selected to replicate real-world 5G network conditions. A simulation duration of one hour was used to enable detailed performance analysis. The model was trained over 16 epochs with a batch size of 64. To minimise overfitting and maintain stable convergence, the hyperparameters were empirically optimised, resulting in a dropout rate of 0.5 and a learning rate of 0.01. Multiple test runs were conducted to identify the best balance between model complexity and predictive accuracy, which ultimately led to the selection of a configuration comprising three hidden layers.

The random waypoint mobility model [35] is used to introduce realistic mobility into the simulation, as shown in Fig. 3.

It has a minimum speed of 1 m/s, a maximum speed of 20 m/s, and a pause period of 0 to 30 s. A one-hour simulation is conducted to allow a detailed performance analysis of the OHMDLNN model. The comparison is based on the packet loss ratio, handover success rate, handover latency, and throughput.

The performance of the proposed model was evaluated using performance measures such as accuracy, loss, precision, and the confusion matrix during validation. It was compared with state-of-the-art deep learning neural network models. With respect to network-specific metrics, the model was assessed based on packet loss ratio, handover latency, handover success rate, and throughput. Further details of these metrics are provided in the results and discussion section.

The OHMDLNN is compared against a set of baseline models, which include both traditional and machine learning approaches. To make the comparison fair, all models were optimized using 100 trials per architecture. This optimization was conducted under the same hyperparameter search settings using a Bayesian optimizer. In each trial, 5-fold cross-validation was applied during training with early stopping (patience = 50). All models were allocated the same computational budget, ensuring that performance differences reflect architectural capabilities rather than tuning advantages. The optimal configurations are presented in Table 4. Thereafter, the performance of the proposed model was tested using our simulated dataset, which comprises 65,000 records generated with Simu5G and originally derived from the DeepSlice dataset [29].

The packet loss rate for MM in 5G networks refers to the percentage of data packets that are not successfully delivered or received during the handover process. The proposed model will predict the need for a handover, thereby reducing the packet loss. This reduction facilitates communication and provides a stable packet transfer.

Table 6 presents the estimated packet loss rate as the number of users increases and compares the results with different network models. Based on Table 6, Fig. 9 illustrates that the lower packet loss rate is achieved by the proposed model with loss rates shown on the Y-axis. The summary of the packet loss rates for the compared models is as follows: RSS-based 2.4%, speed based 2.11%, decision tree 2.13%, clustering 2.19%, fuzzy logic 2.24%, LSTM 2.46%, Bi-RNN 2.41%, RNN 2.50%, CNN 2.50%, GRU 2.50%, ISMM 2.50%, RL 2.50%, Q-ML hybrid 2.50% and the proposed OHMDLNN model has 1.80%.

The high success rates play a significant role in the service continuity and an ideal user experience, particularly in high-mobility situations. This rate is used to measures the proportion of successful handovers out of all handover attempts. An increase in the success percentage proves that the OHMDLNN model is effective at classifying handover situations and minimizing failures in heterogeneous and dense network systems. Table 7 presents the estimated HO success rate with the increase in the number of users and compares the results with other models. According to Table 7, Fig. 10 shows that the success rate of the proposed model is the highest. The summary of the HO success rates for the compared models is as follows: RSS-based 82.2%, speed based 82.2%, decision tree 81.6%, clustering 85.7%, fuzzy logic 84.6%, LSTM 88.0%, Bi-RNN 88.0%, RNN 87.6%, CNN 88.0%, GRU 88.0%, ISMM 84.9%, RL 86.8%, Q-ML hybrid 87.0% and the proposed OHMDLNN model has 90.0%.

HO latency is defined as the time interval from the start of a handover decision until the user equipment connects to the target base station. The HO latency plays a critical role in ensuring continuous service provision and an optimal user experience in high-mobility scenarios. The proposed model accounts for both low and high user mobility ranges since incorrect handover decisions can lead to service interruptions. Furthermore, HO latency is reduced by making accurate handover predictions and executing the process efficiently. Table 8 presents the estimated HO latency with the increase in the number of users and compares it with other models. According to Table 8 and Fig. 11, the HO latency values of the compared models are as follows: RSS-based 48.3 ms, speed based 48.4 ms, decision tree 47.7 ms, clustering 46.9 ms, fuzzy logic 45.6 ms, LSTM 45.2 ms, Bi-RNN 45.3 ms, RNN 43.7 ms, CNN 46.1 ms, GRU 45.1 ms, ISMM 40.6 ms, RL 43.1 ms, Q-ML hybrid 43.5 ms and OHMDLNN 38.0 ms.

The throughput at the time of HO is crucial for continuous service delivery and providing an ideal user experience, especially in high-mobility scenarios. A delayed or wrong handover decision causes packet retransmission and, consequently, low throughput. The proposed model efficiently balances network load and maintains stability during handover, as evidenced by the results. Consequently, it enhances overall network performance. Furthermore, Table 9 presents the estimated throughput with the increase in the number of users and compares these results with other models. Based on Table 9, Fig. 12 illustrates the throughput during HO achieved by the proposed model. A summary of the throughput values for these models, following comparison with others, is as follows: RSS-based 38.2 Mbps, speed based 38.3 Mbps, decision tree 38.0 Mbps, clustering 41.6 Mbps, fuzzy logic 40.8 Mbps, LSTM 45.1 Mbps, Bi-RNN 45.2 Mbps, RNN 45.3 Mbps, CNN 45 Mbps, GRU 45.1 Mbps, ISMM 42.1 Mbps, RL 43.8 Mbps, Q-ML hybrid 44.2 Mbps and the proposed OHMDLNN model 47.9 Mbps.

All reported results correspond to the best-performing configurations identified through the Bayesian optimization process. The consistent superiority of the OHMDLNN across all evaluation metrics reflects the inherent advantages of the proposed architecture rather than tuning effects. Furthermore, the performance of the proposed OHMDLNN model was thoroughly evaluated in a realistic 5G simulation environment. It was compared against several baseline models, including traditional, machine learning, and neural network approaches such as CNN, RNN, LSTM, GRU, Bi-RNN, RL, and ISMM. The results consistently demonstrated that OHMDLNN outperformed all these models across the defined performance metrics. For instance, the OHMDLNN model achieved the lowest packet loss ratio across a wide range of user counts. It started at just 1.80% and remained steady at 1.29% even with up to 900 users. In terms of HO success rate, OHMDLNN reached a peak of 90.0%. This performance surpassed that of CNN 86.0%, RNN 87.0%, and LSTM 87.3%. The model also achieved a significant reduction in packet loss. This finding highlights its applicability in maintaining data integrity during handover events, which is also critical for latency-sensitive applications. In terms of latency, OHMDLNN had the lowest handover delay of 38 ms for 900 users. It performed even better than LSTM (45.2 ms) and Bi-RNN (45.3 ms). These low latencies are especially useful for uRLLC in 5G networks.

Moreover, the OHMDLNN model maintained high throughput even as user numbers increased. This reflects efficient bandwidth utilization in high mobility scenarios. The overall outcomes of the research prove that the model is effective at predicting network transitions while executing handovers efficiently. This improvement in decision-making was possible with the help of network-side KPIs and UE KPIs. These KPIs help mitigate handover failures and ensure uninterrupted service delivery. In addition, the architecture of the model includes a time-convolving structure, dropout, and flattening layers. Such architectural aspects enhance its responsiveness and generalization capability.