Dual connectivity is essential for sixth-generation (6G) and future networks, with carrier aggregation (CA) serving as the key technology that enhances data rates by dynamically allocating multiple frequency carriers to users. However, the constant change of carriers can introduce excessive handovers, leading to signaling overhead and latency issues. In this work, we propose a two-step carrier switching mechanism where the first step predicts the throughput and the number of handovers for given carrier switching decision parameters using different artificial intelligence (AI) mechanisms. The second step is to choose the policy that provides the best trade-off between the number of handovers and the median UE throughput. We utilize real-world traffic data on an industry-grade network simulator to train and test the proposed carrier switching algorithm. We compare four AI models in the first step: random forest, transformer, long-short-term memory (LSTM), and fully connected neural network. Our analysis demonstrates that random forest achieves superior prediction performance for median throughput and average handover frequency due to its robustness against dataset variations. Our results indicate that the number of handovers can be reduced by 40% with a 3% reduction in throughput compared to the handover-unaware baseline.

Stochastic modeling is an essential tool for studying statistical properties of wireless channels. In multipath fading channel (MFC) models, the signal reception is modeled by a sum of wave path contributions, and Clarke's model is an important example of such which has been widely accepted in many wireless applications. However, since Clarke's model is temporally deterministic, Feng and Field noted that it does not model real wireless channels with time-varying randomness well. Here, we extend Clarke's model to a novel time-varying stochastic MFC model with scatterers randomly flipping on and off. Statistical properties of the MFC model are analyzed and shown to fit well with real signal measurements, and a limit Gaussian process is derived from the model when the number of active wave paths tends to infinity. A second focus of this work is a comparison study of the error and computational cost of generating signal realizations from the MFC model and from its limit Gaussian process. By rigorous analysis and numerical studies, we show that in many settings, signal realizations are generated more efficiently by Gaussian process algorithms than by the MFC model's algorithm. Numerical examples that strengthen these observations are also presented.

In WCDMA DC-HSUPA, which allows the user equipment (UE) to simultaneously transmit data over two independent enhanced uplink (EUL) data channels, uplink power control and scheduling of uplink data transmission are independently performed on the two uplink carriers. It has been shown that various factors such as the uplink scheduling strategy, traffic pattern, independent power control, propagation environment etc, cause large power imbalance between the two uplink carriers up to 18-20 dB. This in turn may cause excessive power leakage from the stronger carrier into the weaker carrier resulting in uplink performance loss of the weaker carrier. To mitigate the adverse affect of the power imbalance, the DC-HSUPA capable UE is required to meet the standardized "In-band Emission" requirement of -24 dBc.

Managing multiple services with controlled quality of service (QoS) is crucial to 3/sup rd/ generation cellular system. The real time services, i.e. the traffic classes streaming and conversational, will be the most costly services and therefore most crucial to manage in a spectral efficient manner. This paper proposes and evaluates possible ways to accomplish this for the GSM/EDGE Radio Access Network (GERAN). The evaluation is made with different mixes of voice, WWW browsing and video streaming services, where the latter is based on a novel traffic model for a streaming service using an H.263 codec with suitable settings. The results from the dynamic multiple-cell system simulations show that it is possible to efficiently manage streaming services in GERAN, in single as well as mixed service scenarios, by using service based radio resource management algorithms, e.g., power setting.

A model of streaming video traffic from a streaming server to an end user is proposed. It mimics the codec output (video frames) segmentation into RTP (real time protocol) packets, transmission from the streaming server and reception of packets by the end user. The intention is to use the model in the performance analysis of radio networks. The level of detail is chosen to support the performance analysis of the transport of RTP packets from server to user. The model is based on measurements of H.263 encoded video at various bitrates. The bitrates and the codec settings are chosen to be suitable for mobile access.

Properties of GSM pseudo-random frequency hopping sequences (FHSs) are investigated and it is concluded that they differ significantly from the properties of ideal random sequences. Such discrepancies are evident from correlations as well as collision rates between sequences. It is shown that the collision rate between two FHSs depends heavily on certain FHS parameters such as the hopping sequence number (HSN) and the mobile allocation index offset (MAIO). Another important parameter with significant impact on the collision rate is the frame number (FN) difference between two cells. A consequence is that co-channel interference between cells with fractional loading can be reduced by careful HSN/FN/MAIO planning based solely on collision rate information. This is demonstrated in examples involving two cells.