Abstract
Providing seamless mobility and a uniform user experience, independent of location, is an important challenge for 5G wireless networks. The combination of Coordinated Multipoint (CoMP) networks and Virtual- Cells (VCs) are expected to play an important role in achieving high throughput independent of the mobile’s location by mitigating inter-cell interference and enhancing the cell-edge user throughput. Userspecific VCs will distinguish the physical cell from a broader area where the user can roam without the need for handoff, and may communicate with any Base Station (BS) in the VC area. However, this requires rapid decision making for the formation of VCs. In this paper, a novel algorithm based on a form of Recurrent Neural Networks (RNNs) called Gated Recurrent Units (GRUs) is used for predicting the triggering condition for forming VCs via enabling Coordinated Multipoint (CoMP) transmission. Simulation results, show that based on the sequences of Received Signal Strength (RSS) values of different mobile nodes used for training the RNN, the future RSS values from the closest three BSs can be accurately predicted using GRU, which is then used for making proactive decisions on enabling CoMP transmission and forming VCs.
Highlights
The fifth generation (5G) of mobile networks using mmWave technology is anticipated to deliver a substantial increase in the rates of data traffic over the cellular network, as much as 10 Gbps compared to 100 Mbps in 4G networks [1]
In [11], Wickramasuriya et al used sequences of Received Signal Strength (RSS) values as features for training a Recurrent Neural Network (RNN) classifier based on Long Short-Term Memory (LSTM) to predict the base station (BS) a mobile node will be associated and the optimal Virtual- Cells (VCs) topology according to the mobility of users
Where, g . is non-linear function usually implemented by a hyperbolic tangent, is the logistic sigmoid, 232425are rectangular weight matrices, that are applied to the input x[t] (RSS values from all eight Base Station (BS)), 636465 are square matrices that define the weights of the recurrent connections, 737475 are the bias vectors, and ⨀ is the Hadamard product
Summary
The fifth generation (5G) of mobile networks using mmWave technology is anticipated to deliver a substantial increase in the rates of data traffic over the cellular network, as much as 10 Gbps compared to 100 Mbps in 4G networks [1] Such applications as streaming UltraHigh Definition (UHD) video, Augmented Reality (AR) and Virtual Reality (VR), which have emerged under the 5G Enhanced Mobile Broadband (eMBB) use case, require very high throughput rates everywhere even at the cell edges (i.e., providing a uniform user experience) [1]. In contrast to the cell-centric approach used in traditional cellular networks with fixed cell coverage areas, a new trend of a user-centric approach, where the UE is associated with multiple BSs and creates a Virtual-Cell (VC) [5] that is adapted according to the mobility of UEs. For instance, using joint transmission-CoMP (JTCoMP) in formation of VCs in the downlink (DL) can help to attain a uniform user experience for mobile users via improving the throughput of cell-edge users and minimizing the number of hard handovers.
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