Performance Evaluation of Low Density Spreading Multiple Access

In this paper, we evaluate the performance of Multicarrier-Low Density Spreading Multiple Access (MC-LDSMA) as a multiple access technique for mobile communication systems. The MC-LDSMA technique is compared with current multiple access techniques, OFDMA and SC-FDMA. The performance is evaluated in terms of cubic metric, block error rate, spectral efficiency and fairness. The aim is to investigate the expected gains of using MC-LDSMA in the uplink for next generation cellular systems. The simulation results of the link and system-level performance evaluation show that MC-LDSMA has significant performance improvements over SC-FDMA and OFDMA. It is shown that using MC-LDSMA can considerably reduce the required transmission power and increase the spectral efficiency and fairness among the users

Improving Energy Efficiency for IoT Communications in 5G Networks

Increase in number of Internet of Things (IoT) devices is quickly changing how mobile networks are being used by shifting more usage to uplink transmissions rather than downlink transmissions. Currently, mobile network uplinks utilize Single Carrier Frequency Division Multiple Access (SC-FDMA) schemes due to the low Peak to Average Power Ratio (PAPR) when compared to Orthogonal Frequency Division Multiple Access (OFDMA). In an IoT perspective, power ratios are highly important in effective battery usage since devices are typically resource-constrained. Fifth Generation (5G) mobile networks are believed to be the future standard network that will handle the influx of IoT device uplinks while preserving the quality of service (QoS) that current Long Term Evolution Advanced (LTE-A) networks provide. In this paper, the Enhanced OEA algorithm was proposed and simulations showed a reduction in the device energy consumption and an increase in the power efficiency of uplink transmissions while preserving the QoS rate provided with SC-FDMA in 5G networks. Furthermore, the computational complexity was reduced through insertion of a sorting step prior to resource allocation

Frequency-domain transmit processing for MIMO SC-FDMA in wideband propagation channels

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Joint Multi-Cell Resource Allocation Using Pure Binary-Integer Programming for LTE Uplink

Due to high system capacity requirement, 3GPP Long Term Evolution (LTE) is likely to adopt frequency reuse factor 1 at the cost of suffering severe inter-cell interference (ICI). One of combating ICI strategies is network cooperation of resource allocation (RA). For LTE uplink RA, requiring all the subcarriers to be allocated adjacently complicates the RA problem greatly. This paper investigates the joint multi-cell RA problem for LTE uplink. We model the uplink RA and ICI mitigation problem using pure binary-integer programming (BIP), with integrative consideration of all users' channel state information (CSI). The advantage of the pure BIP model is that it can be solved by branch-and-bound search (BBS) algorithm or other BIP solving algorithms, rather than resorting to exhaustive search. The system-level simulation results show that it yields 14.83% and 22.13% gains over single-cell optimal RA in average spectrum efficiency and 5th percentile of user throughput, respectively.Comment: Accepted to IEEE Vehicular Technology Conference (VTC Spring), Seoul, Korea, May, 201

Scheduling strategies for LTE uplink with flow behaviour analysis

Long Term Evolution (LTE) is a cellular technology developed to support\ud diversity of data traffic at potentially high rates. It is foreseen to extend the capacity and improve the performance of current 3G cellular networks. A key\ud mechanism in the LTE traffic handling is the packet scheduler, which is in charge of allocating resources to active flows in both the frequency and time dimension. In this paper we present a performance comparison of two distinct scheduling schemes for LTE uplink (fair fixed assignment and fair work-conserving) taking into account both packet level characteristics and flow level dynamics due to the random user behaviour. For that purpose, we apply a combined analytical/simulation approach which enables fast evaluation of performance measures such as mean flow transfer times manifesting the impact of resource allocation strategies. The results show that the resource allocation strategy has a crucial impact on performance and that some trends are observed only if flow level dynamics are considered