Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information (CSI) is investigated to provide the required quality of service (QoS) for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original non-convex power minimization problem to a tractable one. Further, a fairness-based beamforming approach is proposed through a max-min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original non-convex problem is reformulated in a linear matrix inequality (LMI) form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the non-robust scheme in terms of the rate satisfaction ratio. Further, simulation results confirm that NOMA consumes a little over half transmit power needed by OMA for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog

A Survey of Downlink Non-orthogonal Multiple Access for 5G Wireless Communication Networks

Accepted by ZTE CommunicationsAccepted by ZTE CommunicationsAccepted by ZTE CommunicationsAccepted by ZTE CommunicationsAccepted by ZTE CommunicationsNon-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique for the next generation cellular communication networks. In this paper, we first discuss a simple NOMA model with two users served by a single-carrier simultaneously to illustrate its basic principles. Then, a more general model with multicarrier serving an arbitrary number of users on each subcarrier is also discussed. An overview of existing works on performance analysis, resource allocation, and multiple-input multiple-output NOMA are summarized and discussed. Furthermore, we discuss the key features of NOMA and its potential research challenges

OFDMA-based resource allocation for wireless communication systems

Ph.DDOCTOR OF PHILOSOPH

Subcarrier and Power Allocation in WiMAX

Worldwide Interoperability for Microwave Access (WiMAX) is one of the latest technologies for providing Broadband Wireless Access (BWA) in a metropolitan area. The use of orthogonal frequency division multiplexing (OFDM) transmissions has been proposed in WiMAX to mitigate the complications which are associated with frequency selective channels. In addition, the multiple access is achieved by using orthogonal frequency division multiple access (OFDMA) scheme which has several advantages such as flexible resource allocation, relatively simple transceivers, and high spectrum efficient. In OFDMA the controllable resources are the subcarriers and the allocated power per subband. Moreover, adaptive subcarrier and power allocation techniques have been selected to exploit the natural multiuser diversity. This leads to an improvement of the performance by assigning the proper subcarriers to the user according to their channel quality and the power is allocated based on water-filling algorithm. One simple method is to allocate subcarriers and powers equally likely between all users. It is well known that this method reduces the spectral efficiency of the system, hence, it is not preferred unless in some applications. In order to handle the spectral efficiency problem, in this thesis we discuss three novel resources allocation algorithms for the downlink of a multiuser OFDM system and analyze the algorithm performances based on capacity and fairness measurement. Our intensive simulations validate the algorithm performances.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Sustainable scheduling policies for radio access networks based on LTE technology

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyIn the LTE access networks, the Radio Resource Management (RRM) is one of the most important modules which is responsible for handling the overall management of radio resources. The packet scheduler is a particular sub-module which assigns the existing radio resources to each user in order to deliver the requested services in the most efficient manner. Data packets are scheduled dynamically at every Transmission Time Interval (TTI), a time window used to take the user’s requests and to respond them accordingly. The scheduling procedure is conducted by using scheduling rules which select different users to be scheduled at each TTI based on some priority metrics. Various scheduling rules exist and they behave differently by balancing the scheduler performance in the direction imposed by one of the following objectives: increasing the system throughput, maintaining the user fairness, respecting the Guaranteed Bit Rate (GBR), Head of Line (HoL) packet delay, packet loss rate and queue stability requirements. Most of the static scheduling rules follow the sequential multi-objective optimization in the sense that when the first targeted objective is satisfied, then other objectives can be prioritized. When the targeted scheduling objective(s) can be satisfied at each TTI, the LTE scheduler is considered to be optimal or feasible. So, the scheduling performance depends on the exploited rule being focused on particular objectives. This study aims to increase the percentage of feasible TTIs for a given downlink transmission by applying a mixture of scheduling rules instead of using one discipline adopted across the entire scheduling session. Two types of optimization problems are proposed in this sense: Dynamic Scheduling Rule based Sequential Multi-Objective Optimization (DSR-SMOO) when the applied scheduling rules address the same objective and Dynamic Scheduling Rule based Concurrent Multi-Objective Optimization (DSR-CMOO) if the pool of rules addresses different scheduling objectives. The best way of solving such complex optimization problems is to adapt and to refine scheduling policies which are able to call different rules at each TTI based on the best matching scheduler conditions (states). The idea is to develop a set of non-linear functions which maps the scheduler state at each TTI in optimal distribution probabilities of selecting the best scheduling rule. Due to the multi-dimensional and continuous characteristics of the scheduler state space, the scheduling functions should be approximated. Moreover, the function approximations are learned through the interaction with the RRM environment. The Reinforcement Learning (RL) algorithms are used in this sense in order to evaluate and to refine the scheduling policies for the considered DSR-SMOO/CMOO optimization problems. The neural networks are used to train the non-linear mapping functions based on the interaction among the intelligent controller, the LTE packet scheduler and the RRM environment. In order to enhance the convergence in the feasible state and to reduce the scheduler state space dimension, meta-heuristic approaches are used for the channel statement aggregation. Simulation results show that the proposed aggregation scheme is able to outperform other heuristic methods. When the aggregation scheme of the channel statements is exploited, the proposed DSR-SMOO/CMOO problems focusing on different objectives which are solved by using various RL approaches are able to: increase the mean percentage of feasible TTIs, minimize the number of TTIs when the RL approaches punish the actions taken TTI-by-TTI, and minimize the variation of the performance indicators when different simulations are launched in parallel. This way, the obtained scheduling policies being focused on the multi-objective criteria are sustainable. Keywords: LTE, packet scheduling, scheduling rules, multi-objective optimization, reinforcement learning, channel, aggregation, scheduling policies, sustainable

Lyapunov Optimization-Based Latency-Bounded Allocation Using Deep Deterministic Policy Gradient for 11ax Spatial Reuse

With the growing demand for wireless local area network (WLAN) applications that require low latency, orthogonal frequency-division multiple access (OFDMA) has been adopted for uplink and downlink transmissions in the IEEE 802.11ax standard to improve the spectrum efficiency and reduce the latency. In IEEE 802.11ax WLANs, OFDMA resource allocation that guarantees latency, called latency-bounded resource allocation, is more challenging than that in cellular networks because severe unmanaged interference from overlapping basic service sets is enhanced due to the concurrent-transmission mechanism newly employed in IEEE 802.11ax. To improve the downlink OFDMA resource allocation with the unmanaged interference caused by IEEE 802.11ax concurrent transmissions, we propose Lyapunov optimization-based latency-bounded allocation with reinforcement learning (RL). We focus on the transmission-queue size for each station (STA) at the access point that determines the STA latency. Using Lyapunov optimization, we formulate the resource-allocation problem with the queue-size constraints in a form that can be solved using RL (i.e., a Markov decision process) and prove the upper bound of the queue size. Our simulation results demonstrated that the proposed method, which uses an RL algorithm with a deep deterministic policy gradient, satisfied the queue-size constraints. This means that the proposed method met the latency requirements, while some baseline methods failed to meet them. Furthermore, the proposed method achieved a higher fairness index than the baseline methods