This paper studies the Joint Resource block (RB) allocation and Power control (JRP) problem for the coexistence of further enhanced mobile broadband (FeMBB) and extreme ultrareliable low latency (eURLLC) services in rate splitting multiple access-based next-generation (e.g., 5G-Advanced and 6G) wireless networks. In the JRP problem, for each FeMBB user, a minimum data rate requirement is considered, and eURLLC users should meet their latency and reliability requirements. To address the JRP problem, we propose a hybrid deep reinforcement learning (HDRL-JRP) algorithm, in which a double dueling deep Q-network is employed for RB allocation and a deep deterministic policy gradient is used for power control. Via simulation results, the performance of the HDRL-JRP algorithm is demonstrated in terms of total data rate.

We study the problem of end-to-end (E2E) network slicing, i.e., joint slicing of the radio access network (RAN) and core network (CN), for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC) services in future generation cellular (e.g., 5G-Advanced/6G) networks. The E2E resource slicing problem is defined as a mixed-integer non-linear programming problem to minimize the E2E energy consumption and the cost of utilized resources. To overcome the difficulty of solving this problem, we decompose it into two sub-problems, namely, RAN resource allocation (RRA) and CN resource allocation (CRA) problems. In both RRA and CRA problems, the existence of binary variables makes them intractable. To tackle this difficulty, we relax the binary variables by introducing penalty functions. Then, we make the RRA and CRA problems convex by employing the majorization-minimization approximation method. Via simulation results, we compare our proposed joint RAN and CN resource allocation algorithm (JRCRA) with the disjoint solution where RAN and CN resources are allocated to users separately. The joint allocation of resources in the RAN and CN has the advantage that the E2E tolerable latency of users can be flexibly divided between RAN and CN. In contrast, if resources in RAN and CN are allocated separately, a predefined part of the E2E tolerable latency should be considered as the tolerable latency in RAN and CN. The simulation results illustrate that our proposed JRCRA algorithm obtains a 34% improvement in energy consumption and a 24% improvement in cost compared to the disjoint one. Moreover, via simulation results, we illustrate that in comparison with existing algorithms, our proposed JRCRA obtains a higher performance. Besides, simulation results confirm that JRCRA reaches a close performance to the optimal solution.

End-to-End (E2E) network slicing enables wireless networks to provide diverse services on a common infrastructure. Each E2E slice, including resources of radio access network (RAN) and core network, is rented to mobile virtual network operators (MVNOs) to provide a specific service to end-users. RAN slicing, which is realized through wireless network virtualization, involves sharing the frequency spectrum and base station antennas in RAN. Similarly, in core slicing, which is achieved by network function virtualization, data center resources such as commodity servers and physical links are shared between users of different MVNOs. In this paper, we study E2E slicing with the aim of minimizing the total energy consumption. The stated optimization problem is non-convex that is solved by a sub-optimal algorithm proposed here. The simulation results show that our proposed joint power control, server and link allocation (JPSLA) algorithm achieves 30% improvement compared to the disjoint scheme, where RAN and core are sliced separately.

Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators’ OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users’ service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users’ SFCs without the need for an NFVO. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously. To address this problem, we propose an Approximation-based Resource Allocation algorithm (ARA) using Majorization-Minimization approximation method to convexify NFV-RA problem. Furthermore, due to the high complexity of ARA algorithm, we propose a low complexity Hungarian-based Resource Allocation (HuRA) algorithm using Hungarian algorithm for server allocation. Through the simulation results, we show that our proposed ARA and HuRA algorithms achieve near-optimal performance with lower computational complexity. Also, ARA algorithm outperforms the existing algorithms in terms of number of active servers, energy consumption, and average latency. Moreover, the mining process is the foundation of BC technology. In wireless networks, mining is performed by resource-limited mobile users. Since the mining process requires high computational complexity, miner users cannot perform it alone. So, in this article, we assume that miner users can perform mining process with participating of other users. For mining process, the problem of minimizing the energy consumption and cost of users’ processing resources is formulated as a linear programming problem that can be optimally solved in polynomial time.

In this paper, we study the problem of minimizing the uplink aggregate transmit power subject to the users' minimum data rate and peak power constraint on each sub-channel for multi-cell wireless networks. To address this problem, a distributed sub-optimal joint power and rate control algorithm called JPRC is proposed, which is applicable to both non-orthogonal frequency-division multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) schemes. Employing JPRC, each user updates its transmit power using only local information. Simulation results illustrate that the JPRC algorithm can reach a performance close to that obtained by the optimal solution via exhaustive search, with the NOMA scheme achieving a 59% improvement on the aggregate transmit power over the OFDMA counterpart. It is also shown that the JPRC algorithm can outperform existing distributed power control algorithms.