With the proliferation of cellular networks, there has been an unprecedented surge in multimedia mobile data traffic. Managing this massive data traffic has become increasingly intricate due to limited resource availability. To address this limitation and for improved resource utilization in cellular networks, multicast transmission is considered a promising technology due to its capability to serve multiple users via shared radio resources. However, as we show in this article, a blanket switching from unicast to multicast transmission without consideration to the channel condition of users may not achieve the most efficient result. In order to achieve an efficient resource utilization in cellular networks, we propose a DYnamic Resource allocation scheme for converged Unicast and Multicast (DY-RUM) transmissions considering the channel condition of users. The scheme has been proposed in the context of the Fifth Generation (5G) mobile communication system. To implement this scheme, we use the Open Radio Access Network (O-RAN) architecture for the 5G system, which provides a suitable setting for the scheme. We also present simulation results affirming the effectiveness of the proposed scheme in improving resource utilization for converged unicast-multicast transmissions in 5G.

In today's wireless networks, a variety of Radio Access Technologies (RATs) are present. However, each RAT being controlled individually leads to suboptimal utilization of network resources. Due to the remarkable growth of data traffic, interworking among different RATs is becoming necessary to overcome the problem of suboptimal resource utilization. Users can be offloaded from one RAT to another based on loads of different networks, channel conditions and priority of users. We consider the optimal RAT selection problem in a Fifth Generation (5G) New Radio (NR)-Wireless Fidelity (WiFi) network where we aim to maximize the total system throughput subject to constraints on the blocking probability of high priority users and the offloading probability of low priority users. The problem is formulated as a Constrained Markov Decision Process (CMDP). We reduce the effective dimensionality of the action space by eliminating the provably suboptimal actions. We propose low-complexity online heuristics for RAT selection which can operate without the knowledge regarding the statistics of system dynamics. Network Simulator-3 (ns-3) simulations reveal that the proposed algorithms outperform traditional RAT selection algorithms under realistic network scenarios including user mobility.

To support the data requirements of exponentially increasing number of cellular users, Internet of Things (IoT) devices, and enterprises, wireless cellular networks are undergoing significant architectural enhancements. The heterogeneous network architecture is one such advancement, which can augment the capacity of cellular networks through the addition of femto/pico (small) cells. However, fiber connectivity to each small cell is not feasible. In such scenarios, wireless backhaul enables connectivity between small cells and core network (CN). Integrated Access and Backhaul (IAB) has emerged as a solution in 5G network, where wireless backhauling is supported. In IAB networks, IAB-donors are connected to the CN through fiber connectivity, and the multiple IAB-nodes are associated with IAB-donors through wireless backhaul. IAB Nodes can support small cells and provide last mile connectivity to users and IAB-donors act as wireless backhaul provider. For efficient utilization of the spectrum in wireless backhaul, we design an auction-based mechanism to allocate resources dynamically across IAB-nodes considering the spatial and temporal variation of the network traffic. Moreover, using Monte Carlo simulations, we show that the proposed mechanism achieves optimal social welfare.

IAB is a feasible and economical solution to deploy ultra-dense cells in the 5G networks, where access and wireless backhaul links share the same spectrum. IAB eliminates the need to connect ultra-dense cells to the core network through the wired backhaul. However, mmWave backhauling and multihop topology imposes new constraints on the 5G IAB network that become a hindrance to its effective performance. In this paper, we elaborate on cell selection and present a few cell selection policies designed explicitly for IAB networks. Unlike the popular RSRP based policy that may lead to load unbalance in the IAB network, the proposed policies are devised after considering the backhaul constraints and end-to-end performance requirements. The performance of these policies is investigated using system-level simulations. These policies have shown tremendous improvement in achieving cell-edge throughput while maintaining comparable average UE throughput. The policies provide better load balancing and topology in the IAB network than the RSRP based policy.

Proportional Fair (PF) is a scheduling technique to maintain a balance between maximizing throughput and ensuring fairness to users. Dual Connectivity (DC) technique was introduced by the 3rd Generation Partnership Project (3GPP) to improve the mobility robustness and system capacity in heterogeneous networks. In this paper, we demonstrate the utility of DC in improving proportional fairness in the system. We propose a low complexity centralized PF scheduling scheme for DC and show that it outperforms the standard PF scheduling scheme. Since the problem of dual association of users for maximizing proportional fairness in the system is NP-hard, we propose three heuristic user association schemes for DC. We demonstrate that DC, along with the proposed PF scheme, gives remarkable gains on PF utility over single connectivity and performs almost close to the optimal PF scheme in heterogeneous networks.

Dual Connectivity (DC) is one of the key techniques to harness the potential of heterogeneous cellular networks. However, 3rd Generation Partnership Project (3GPP) has introduced disparate mechanisms for DC support in different Radio Access Technologies (RATs), bringing complexity to the network nodes in a Multi-RAT Radio Access Network (RAN). Moreover, DC support requires an exchange of high volume of control information between these network nodes. To address these issues, in this paper an SDN based architecture for Multi-RAT RAN is proposed. The proposed architecture brings simplicity to the network interactions, allows flexibility in the network and helps the network in performing load balancing and mobility management functions effectively. We also demonstrate a reduction in the control signaling and an improvement in system performance compared to legacy architecture.

Today's wireless networks consist of a multitude of Radio Access Technologies (RATs), each being controlled individually, leading to suboptimal utilization of network resources. However, the unprecedented growth of data traffic is creating the need for an efficient inter-working of various RATs to circumvent the problem of suboptimal utilization of resources. Application of Software Defined Networking (SDN) principles enables the control and management of various RATs in a unified way. In this paper, we specifically focus on the inter-working between Long Term Evolution (LTE) and Wireless Fidelity (WiFi). We propose an SDN based architecture for a network comprising LTE Base Stations (BSs) and WiFi Access Points (APs). Users can be offloaded from one RAT to another based on different criteria, viz., user priority and channel state of users. We consider the problem of optimal RAT selection to maximize the total system throughput subject to constraints on the blocking probability of high priority users and the offloading probability of high priority users and formulate it as a Constrained Markov Decision Process (CMDP). We propose a low-complexity RAT selection algorithm which does not require the knowledge of the statistics of system dynamics. To conduct experiments, we develop a Network Simulator-3 (ns-3) based evaluation platform in accordance with the SDN principles. Experimental results demonstrate that the proposed algorithm provides a near-optimal performance.