Massive Carrier Aggregation Research Articles

NB-IoT and eMTC: Engineering results towards 5G/IoT Mobile Technologies

5G RF technology is an essential part of the wireless mobile technology. Technically, 5G is predicted to empower people-thing and thing-thing interconnections by combining wireless technologies and networks. Our university has been actively support research on 4G/5G/IoT, in which the NB-IoT, eMTC and Massive Carrier Aggregation new features challenge are of big interest. New 5G trials put forward a lot of prerequisites for new RF interface in terms of radio bandwidth, power issues, as well as the huge number of connections for IoT capacity management and optimization. Based on Morocco’s current situation, this paper first discusses the 5G technologies; In addition, we will discuss IoT SoftRadio application support, and NB-IoT master plan recap. At last, based on the current plan of 5G RF KPIs management according to first Morocco 5G trail Results and Deployment plans.

Energy Efficient Power Allocation for Carrier Aggregation in Heterogeneous Networks: Partial Feedback and Circuit Power Consumption

The energy-efficient power allocation for carrier aggregation (CA) in heterogeneous networks (HetNets) is studied in this paper. We assume that multiple component carriers belong to HetNets and, thus, inter-band CA is considered, where both full and partial feedback scenarios are investigated. For the latter, we consider quantized channel state information at the transmitter and the corresponding power allocation policies are derived. Next, we derive energy-efficient optimal power allocation policies by assuming circuit power consumption for the transmitter that account for the multiple radio frequency (RF) chains required to support massive inter-band CA. The resulting algorithms are then used in order to investigate the relation between optimal energy efficiency (EE) and design parameters such as the number of frequency bands, antennas and feedback allocation through controlling the quantized information. A greedy optimization algorithm is also introduced to jointly optimize the aforementioned parameters. By carrying out simulations, it is observed that the achievable EE gains for massive CA in HetNets is further limited by either the number of feedback bits or the increase in RF circuits power consumption.

Joint Interference Mitigation and Data Recovery for Massive Carrier Aggregation via Non-Linear Compressive Sensing

Due to the demand for higher throughput, there is need for aggregating more carriers to serve one user equipment. Massive carrier aggregation (MCA) may help as it aggregates a large number of potentially non-contiguous carriers spanning a wide bandwidth. However, implementing MCA brings challenges to the design of the receiver and corresponding data recovery algorithms. For example, if we assign a separate receiver chain for each carrier, the number of receiver chains will be large, which imposes a huge cost. If we use a single receiver chain for all non-contiguous carriers, an expensive high rate analog-to-digital converter (ADC) is required to sample the entire span of the carriers. To reduce the cost, we propose a receiver architecture that employs only one receiver chain with a non-uniform ADC, whose sampling rate is much smaller than the Nyquist rate, and a low cost power amplifier with small dynamic range. Under such architecture, the received signal suffers from non-linear distortion and interference, and the resulting data recovery is a challenging non-linear compressive sensing problem. We propose an algorithm to jointly mitigate the interference and recover the data, which is proved to have theoretical performance guarantees and verified advantageous over baselines in simulations.

Low Complexity Resource Allocation for Massive Carrier Aggregation

Optimal resource allocation (RA) in massive carrier aggregation scenarios is a challenging combinatorial optimization problem whose dimension is proportional to the number of users, component carriers (CCs), and OFDMA resource blocks per CC. Toward scalable, near-optimal RA in massive CA settings, an iterative RA algorithm is proposed for joint assignment of CCs and OFDMA resource blocks to users. The algorithm is based on the principle of successive geometric programming approximations and has a complexity that scales only linearly with the problem dimension. Although its derivation is based on a relaxed formulation of the RA problem, the algorithm is shown to converge to integer-valued RA variables with probability 1 under mild assumptions on the distribution of user utilities. Simulations demonstrate improved performance of the proposed algorithm compared to commonly considered heuristic RA procedures of comparable complexity.

Massive carrier aggregation in LTE-advanced pro: impact on uplink control information and corresponding enhancements

3GPP began standardization of carrier aggregation (CA) in LTE-A Rel. 10 to combine up to five component carriers (CCs) for facilitating wider transmission bandwidth of 100 MHz. Further enhancements followed in Rel. 11 and Rel. 12 to enable features such as aggregation of carriers between time-division duplex (TDD) and frequency-division duplex (FDD) frame structures. In LTE-A Pro Rel. 13, massive CA enhancements were defined to combine more than five CCs with the initial focus on the downlink. While the benefits of CA have been evident, operating an even larger number of downlink carriers results in increased overhead on the uplink as there is a need to send the combined control information for all the CCs. In this article we give an overview of the standardized enhancements, their impact on uplink control information overhead and its transmission, new control channel formats and their link level investigation in 3GPP-defined simulation assumptions.

A Filter-Bank Transceiver Architecture for Massive Non-Contiguous Carrier Aggregation

Current application of carrier aggregation (CA), a key enabling technology for wideband communications over fragmented spectrum, is limited to only a small number of component carriers (CCs). However, wireless broadband connectivity of future networks requires the exploitation of multiple, contiguous, and non-contiguous CCs (NC-CCs). By considering a large number of aggregated NC-CCs, the concept of massive non-contiguous CA (NC-CA) is realized, which offers the highest flexibility in spectrum aggregation. In order to fully exploit the system-level benefits of massive NC-CA, transceivers capable of efficiently accommodating parallel transmissions over all of the available NC-CCs are required. This is a challenging task in which current NC-CA transceiver architectures, including the straightforward parallel chains implementation, fail to accomplish, as they suffer from scalability and efficiency issues. Focusing on the digital baseband processing part of the transceiver, this paper proposes an efficient filter-bank transceiver architecture able to accommodate a large number of NC-CCs with a small complexity increase compared with the ideal case of the transceiver operating on a single CC with the same aggregate bandwidth. Specification of this new architecture is cast as a filter design problem with minimal assumptions on transmitted waveform format, channel statistics, and decoding scheme, allowing for universally applicable designs. Both heuristic and optimal design methodologies are provided, the latter obtained using the notion of capacity mean square error as an appropriate performance/design metric. The performance of the proposed architecture is evaluated analytically, and a case study where it is applied for OFDM transmissions is provided.

Low-Complexity and Low-Feedback-Rate Channel Allocation in CA MIMO Systems with Heterogeneous Channel Feedback

In this paper, we propose a low complexity and low-feedback-rate channel allocation for carrier aggregation (CA) MIMO systems as an application to heterogeneous networks (HetNets) with multiple component carriers (CCs) and users. The proposed channel allocation technique relies on the heterogeneous channel feedback concept due to the heterogeneous channel statistics of the different CCs. We apply the heterogeneous channel feedback, where the channel feedback granularity is provided using best- $M$ selected feedback strategy. The precoding matrix indicator (PMI) and rank indicator (RI) are designed with a low complexity so that the PMI/RI reports are calculated more efficiently per CC and user. This approach reduces the complexity significantly in a multiuser and multichannel scenario. Next, we deal with the low-feedback rate and thus devise a channel allocation problem. The problem on one hand provides the minimization of the feedback overhead and, on the other hand, it guarantees the quality-of-service request of each user for a specific target throughput. The problem is considered as a channel assignment problem; thus, the Hungarian method is first used. Next, a low complexity solution is devised using the stable matching algorithm. A complexity analysis and evaluation among the original and the proposed approach for the low-complexity and the low-feedback-rate channel allocation is carried out. This comparison reveals the benefits of the proposed solution for CA MIMO system in HetNets. The proposed approach can be considered as a proposal to the future massive CA, i.e., up to 32 CCs.