Integrated Fractional Load and Packet Scheduling for OFDMA Systems

This paper analyzes the use of Random Frequency Hopping to enable self-organizing Orthogonal Frequency Division Multiple Access systems (RFH-OFDMA). While LTE (Long Term Evolution) macrocell resource planning is typically based on centralized planning of orthogonal deterministic frequency hopping patterns, the integration of femtocells located within macrocells introduces either complex planning efforts or uncontrolled interference issues. The results presented in this paper show that random frequency hopping is a new effective way to reduce interference for the integration of femtocells without resource planning into macrocells. An analytical model for the SINR (Signal-to-Interference-and-Noise Ratio) and a simulation model for the BER (Bit Error Rate) are presented for interfering RFH-OFDMA systems. All OFDMA parameters can be freely selected in time and frequency in the model, enabling to dimension systems with minimal interference. Based on the analytical model, a performance evaluation is presented, which uses typical LTE parameters. Compared to the performance of systems without resource planning and random frequency hopping, a significant gain is achieved for self-organizing RFH-OFDMA systems with respect to the SINR and BER.

We consider the allocation of spectral and power resources every subframe (1~ms) on the uplink of the Long Term Evolution (LTE) wideband orthogonal frequency division multiple access (OFDMA) cellular network. System bandwidth is divided into multiple sub-bands. The fractional power control to manage interference is allowed to be a function of the sub-band. Moreover, the channel gains from the base-station to the mobiles (user equipment (UE) in LTE) can vary with the sub-band. The power and spectral resources are to be allocated among different UEs in a cell based on channel conditions, desired fairness/QoS, and constraints on power due to interference and power limitation at a UE. For $N$ UEs and $M$ sub-bands, we exploit the structure of the underlying optimization problem to obtain an $O(NM)$ algorithm for optimal resource allocation when a UE can be allocated resources in a non-contiguous manner in frequency. To implement this in an OFDMA system, we delineate a simple control signalling structure where the UE computes in $O(M)$ time a waterfilling solution every subframe it is assigned resources. When the allocation is restricted to contiguous allocation in frequency due to single carrier constraint in LTE, we convert the solution computed above in $O(NM)$ time into a feasible solution. For typical network scenarios, the solution thus computed is numerically shown to be very close to the optimal solution with no restrictions.

This paper presents the link-level performance of downlink non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) receiver, considering the influence of error vector magnitude (EVM). In our evaluations, for the case of a 1-by-2 transmission and the number of NOMA multiplexed users is two, we clarify the performance of NOMA for cell-center user equipment (UE) with codeword level SIC (CWIC) receiver and the performance of the cell-edge UE, which does not apply SIC. Based on link-level performance evaluations, we show that the performance of CWIC receiver is almost the same as that of ideal SIC when the allocated transmit power ratio to cell-center UE is less than 0.35, the modulation and coding scheme (MCS) of cell-center UE is 16QAM (R = 0.51) or 64QAM (R = 0.47), and the MCS of cell-edge UE is QPSK (R = 0.51). Furthermore, we also show that, when EVM value is 4 % and the allocated power ratio to cell-center UE is between 0.2 and 0.37, the level of degradation due to the influence of EVM is below 0.6 dB, which is almost the same level of degradation as that of orthogonal frequency division multiple access (OFDMA) for the EVM value required by 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE). On the other hand, we show that the influence of EVM is marginal for cell-edge UE compared to cell-center UE. However, when the cell-center UE uses a higher order modulation such as 64QAM, we show that the required SNR of cell-edge UE increases due to the increase in inter-user interference. Based on these results, we clarify that it is important for the base station (BS) to take into account the target BLER and influence of EVM when selecting the MCS and allocated transmit power ratio of NOMA UEs.

Deployment of relay nodes (RNs) in cellular orthogonal frequency division multiple access (OFDMA) systems provides an effective solution to increase high data rate coverage and improve cell throughput. However, a challenging issue is that additional interferences caused by RNs may substantially compromise the performance gain if no measure is taken. In this study, the authors address the problem of interference coordination in relay-aided cellular OFDMA systems, aiming at exploiting the benefits of RNs while minimising the negative effects of interferences introduced. The authors first analyse the possible interference scenarios in multi-cell systems. Based on the insights into their analysis, the authors propose resource allocation with interference coordination (RAIC) scheme for cellular OFDMA systems. RAIC selectively perform one of three resource allocation algorithms according to the offered traffic load in the system, to mitigate interferences and thus enhance system throughput. The authors conduct intensive simulation experiments based on the model with realistic broadband channel propagation conditions. Numerical results demonstrate that their proposed RAIC can effectively improve system throughput compared with the resource allocation schemes without appropriate interference coordination.

Channel quality feedback is crucial for the operation of 4G and 5G radio networks, as it allows to control User Equipment (UE) connectivity, transmission scheduling, and the modulation and rate of the data transmitted over the wireless link. However, when such feedback is frequent and the number of UEs in a cell is large, the channel may be overloaded by signalling messages, resulting in lower throughput and data loss. Optimizing this signaling process thus represents a key challenge. In this paper, a focus on Channel Quality Indicator (CQI) reports that are periodically sent from a UE to the base station, and propose mechanisms to optimize the reporting process with the aim of reducing signaling overhead and avoiding the associated channel overloads, particularly when channel conditions are stable. To this end, we apply machine learning mechanisms to predict channel stability, which can be used to decide if the CQI of a UE is necessary to be reported, and in turn to control the reporting frequency. For this purpose,four machine learning models,namely Support Vector Machines (SVM),K-Nearest Neighbour (KNN),Random Forest and Neural Networks (ANN). Simulation results shows that both provide a high prediction accuracy, with ANN consistently outperforming, especially as CQI reporting frequency reduces. Keywords: ANN,KNN,SVM,Random Forest,CQI,5G,UE

In this letter, a novel resource allocation method is proposed for Discrete Fourier Transform Spread Orthogonal Frequency Division Multiple Access (DFT-s-OFDMA) systems in Long Term Evolution (LTE). The proposed method is developed based on a minimal metric loss criterion and performs better than the commonly used Recursive Maximum Expansion (RME) method.

Ultra dense network of small cells (SCs) was introduced to support high data rate services and increase network capacity. Owing to user equipment (UE) mobility and limited coverage of SCs, the load across a SC network recurrently becomes unbalanced. Such unbalanced loads result in performance degradation of network in terms of throughput, inefficient utilization of network resources and link failure. This paper proposes and evaluates a novel load balancing methodology based on UE-SC association, which incorporated two main factors:(i)load of SCs in UE’s neighborhood (ii)the received Signal to Interference and Noise Ratio (SINR)/Channel Quality Indicator (CQI) and Quality of Service (QoS) requirements of user. The proposed algorithm ensures that the standard deviation of load in SCs in the neighborhood of a given UE remains minimum when the UE is associated to a potential SC. Consequently, achieving load balancing among SCs proactively while maintaining high SINR/CQI levels for UE, with each UE-SC pairing. System-level simulations show that the proposed algorithm provides a more balanced load across network, which is quantified by a smaller standard deviation of load across the SCs and higher network throughput when compared to other load balancing algorithms.

Long Term Evolution (LTE) network deploys Orthogonal Frequency Division Multiple Access (OFDMA) technology for downlink multi-carrier transmission. To meet the Quality of Service (QoS) requirements for LTE networks, packet scheduling has been employed. Packet scheduling determines when and how the user’s packets are transmitted to the receiver. Therefore effective design of packet scheduling algorithm is an important discussion. The aims of packet scheduling are maximizing system throughput, guaranteeing fairness among users, andminimizing either or both PacketLoss Ratio (PLR)and packet delay. Inthis paper, the performance of two packet scheduling algorithms namely Log Maximum-Largest Weighted Delay First (LOG-MLWDF) and Max Delay Unit (MDU), developed for OFDM(Orthogonal Frequency Division Multiplexing)networks, has been investigated in LTE downlink networks, and acomparison of those algorithmswith a well-known scheduling algorithm namely Maximum-Largest Weighted Delay First(MLWDF) has been studied.The performance evaluation was in terms of system throughput, PLR and fairness index. This study was performed forboth real time (voice and video streaming)and non-real time (best effort)perspectives. Results show that for streaming flows,LOG-MLWDF shows best PLR performance among the considered scheduling schemes, and for best effort flows, it outperforms theother two algorithms in terms of packet delay and throughput.

Orthogonal Frequency-Division Multiple Access (OFDMA) systems have attracted considerable attention through technologies such as 3GPP Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). OFDMA is a flexible multiple-access technique that can accommodate many users with widely varying applications, data rates, and Quality of Service (QoS) requirements. OFDMA has the advantages of handling lower data rates and bursty traffic at a reduced power compared to single-user OFDM or its Time Division Multiple Access (TDMA) or Carrier Sense Multiple Access (CSMA) counterparts. In our work, we propose a Particle Swarm Optimization based resource allocation and scheduling scheme (PSORAS) with improved quality of service for OFDMA Systems. Simulation results indicate a clear reduction in delay compared to the Frequency Division Multiple Access (FDMA) scheme for resource allocation, at almost the same throughput and fairness. This makes our scheme absolutely suitable for handling real time traffic such real time video-on demand.

Two novel packet scheduling algorithms, the basic and modified priority and fairness (BPF and MPF)-based packet scheduling algorithms, are proposed in this paper for adaptive modulation and coding (AMC)-based orthogonal frequency division multiple access (OFDMA) systems. In the proposed algorithms, various user traffics are transmitted in a sequence determined by their priorities on subchannels while jointly considering fairness among users and subchannels. Simulation results show that the proposed algorithms increase the number of supported users satisfying the same underlying quality of service (QoS) requirements than some existing packet scheduling algorithms in literature.

The Long Term Evolution (LTE) standards are generated by the Third Generation Partnership Project (3GPP) for the Fourth Generation (4G) wireless communication systems. The Fourth Generation (4G) wireless communication standards use new physical layer signaling techniques in their physical layer. Single Carrier Frequency Division Multiple Access (SC-FDMA) and Orthogonal Frequency Division Multiple Access (OFDMA) are the two major signaling techniques used in LTE. OFDMA is used in the downlink of the Fourth Generation (4G) wireless communication systems for its high spectral efficiency and SC-FDMA is used in the uplink of the Fourth Generation (4G) wireless communication systems for its high power efficiency. The main objective of this paper is to design and implement the reliable and energy efficient architecture for LTE uplink system using FPGA. IFDMA is the most energy efficient subcarrier mapping scheme of SC-FDMA multiple access scheme. In IFDMA, the data symbols subcarrier mapping is not one to one instead each subcarrier carries a part from each data symbol transmitted at that time instant. The reason for the selection of IFDMA subcarrier mapping along with SC-FDMA multiple access scheme for LTE uplink comes mainly from its advantage to provide a low peak to average power ratio (PAPR) for the transmitted waveform. This results in less power consumption in the mobile station during uplink transmission. The reliability of the system is improved using interleaving process by avoiding packet loss. Convolution codes are less complex and are used to provide error free transmission. The enhancement of energy efficiency is done using the convolution coded IFDMA. The chosen energy efficient architecture must be more power efficient and support high data rates, which is the main requirement for the mobile user. The proposed reliable and energy efficient architecture for LTE uplink system is implemented using Xilinx system generator tool.

The overall performance of Orthogonal Frequency Division Multiple Access (OFDMA) system is greatly affected by the peak-to-average power ratio(PAPR). To address the PAPR problem, a tone reservation(TR) method based on FFT/IFFT structure is discussed, then we propose to replace FFT by the fast discrete cosine transform(DCT) which could reduce the computational complexity. Moreover, the experiments prove that the system bit error rate(BER) will not be affected and higher PAPR reduction can be achieved.

Orthogonal frequency division multiple access (OFDMA) and Single Carrier Frequency Division Multiple Access (SC-FDMA) are the two major multiple access schemes for 4G wireless communications. In long term evolution (LTE) the downlink multiple access scheme is based on OFDMA and the uplink multiple access scheme is based on SC-FDMA. In this letter, we derive the fundamental performance difference between OFDMA and SC-FDMA, and present the general performance comparison between them. Analytical results show that OFDMA performance upper bounds SC-FDMA in the sense of capacity. The conclusion is further confirmed by numerical results.

Long Term Evolution (LTE) is one of the standards in 4G cellular technologies which offer different data rates for voice, video conference and other services. The Orthogonal Frequency Division Multiple Access (OFDMA) is a well suited multiple access scheme of LTE for supporting variable bit rate services. OFDMA-based Cellular communication network face serious challenges in providing good Quality of Service (QoS) to the users. In order to provide good QoS, network operators have to make the effective use of their available resources, which leads to effective network design and network planning. Erlang capacity is the dominant parameter used for effective network design and network planning. Co-channel Interference is an important limiting factor for Erlang capacity in OFDMA-based cellular systems. The capacity of a OFDMA cellular network is determined by the amount of CCI it can tolerate. In this paper a new method is proposed for the evaluation of Erlang capacity of a OFDMA system under CCI. The Erlang capacity of OFDMA-based cellular systems under CCI is evaluated and compared for different number of co-channel interferers, various spectral efficiencies, voice activity factors and various data rates.

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