arXiv subjects

Chandan Pradhan

Publications and source records attributed to Chandan Pradhan.

39 records · Page 3Linked to original sources

arXiv2015

Full-Duplex Transceiver for Future Cellular Network: A Smart Antenna Approach

In this paper, we propose a transceiver architecture for full-duplex (FD) eNodeB (eNB) and FD user equipment (UE) transceiver. For FD communication,.i.e., simultaneous in-band uplink and downlink operation, same subcarriers can be allocated to UE in both uplink and downlink. Hence, contrary to traditional LTE, we propose using single-carrier frequency division multiple accesses (SC-FDMA) for downlink along with the conventional method of using it for uplink. The use of multiple antennas at eNB and singular value decomposition (SVD) in the downlink allows multiple users (MU) to operate on the same set of ubcarriers. In the uplink, successive interference cancellation with optimal ordering (SSIC-OO) algorithm is used to decouple signals of UEs operating in the same set of subcarriers. A smart antenna approach is adopted which prevents interference, in downlink of a UE, from uplink signals of other UEs sharing same subcarriers. The approach includes using multiple antennas at UEs to form directed beams towards eNode and nulls towards other UEs. The proposed architecture results in significant improvement of the overall spectrum efficiency per cell of the cellular network.

cs.NI↗

arXiv2015

Full-Duplex eNodeB and UE Design for 5G Networks

The recent progress in the area of self-interference cancellation (SIC) design has enabled the development of full-duplex (FD) single and multiple antenna systems. In this paper, we propose a design for FD eNodeB (eNB) and user equipment (UE) for 5G networks. The use of FD operation enables simultaneous in-band uplink and downlink operation and thereby cutting down the spectrum requirement by half. FD operation requires the same subcarrier allocation to UE in both uplink and downlink. Long Term Evolution LTE) uses orthogonal frequency division multiple access (OFDMA) for downlink. To enable FD operation, we propose using single carrier frequency division multiple access SC-FDMA) for downlink along with the conventional method of using it for uplink. Taking advantage of channel reciprocity, singular value decomposition (SVD) based eamforming in the downlink allows multiple users (MU) to operate on same set of subcarriers. In uplink, frequency domain minimum mean square error (MMSE) equalizer along with successive interference cancellation with optimal ordering (SSIC-OO) algorithm is used to decouple signals of users operating in the same set of subcarriers. The work includes simulations showing efficient FD operation both at UE and eNB for downlink and uplink respectively.

cs.IT↗

arXiv2015

Machine Learning Based Cooperative Relay Selection in Virtual MIMO

In cellular systems, virtual multiple-input multiple-output (V-MIMO) technology promises to achieve performance gains comparable to conventional MIMO. In this paper, we propose cooperative relay selection algorithm based on machine learning techniques. Willingness of user to cooperate in V-MIMO depends on his current battery power, time and day along with incentives offered by service provider. Every user has different criterion to participate in V-MIMO, but allows a specific behavior pattern. Therefore, it is required to predict willing users in the neighborhood of source user (SU), before selecting users as cooperative nodes. Only inactive users belonging to Virtual Antenna Array (VAA) cell of SU are assumed to cooperate. This reduces control overheads in cooperative node discovery. In this paper, we employ prediction algorithm using two machine learning techniques i.e. ANN and SVM to find out inactive willing users within VAA cell. The parameters such as MSE, accuracy, precision and recall are calculated to evaluate performance of ANN and SVM model. Prediction using ANN has MSE of 3% with average accuracy of 97% (variance 0.37), whereas SVM has MSE of 2.58% with average accuracy of 97.56% (variance 0.17). We also observe that proposed prediction method reduces the node discovery time by approximately 29%.

cs.NI↗