Underwater acoustic sensor networks (UW-ASNs) have recently been proposed for exploring the underwater resources and gathering the scientific data from the aquatic environments. UW-ASNs are faced with different challenges, such as high propagation delay, low bandwidth, and high energy consumption. However, the most notable challenge is perhaps how to efficiently forward the packets to the surface sink by considering the energy constrained sensor devices. The opportunistic routing concept may provide an effective solution for the UW-ASNs by the cooperation of the relay nodes to forward the packets to the surface sink. In this paper, the energy consumption problem is addressed and an energy-efficient cooperative opportunistic routing (EECOR) protocol is proposed to forward the packets toward the surface sink. In the EECOR protocol, a forwarding relay set is firstly determined by the source node based on the local information of the forwarder and then, a fuzzy logic-based relay selection scheme is applied to select the best relay based on considering the energy consumption ratio and the packet delivery probability of the forwarder. In the UW-ASNs, most of the energy is wasted due to the collisions amongst sensor nodes during the packet transmission. To alleviate the packet collisions problem, we have designed a holding timer for each of the forwarder to schedule the packets transmission toward the surface sink. We have performed our extensive simulations of the EECOR protocol on the Aqua-sim platform and compared with existing routing protocols in terms of average packet delivery ratio, average end-to-end delay, average energy consumption, and average network lifetime.

Towards supporting a myriad of new services and applications under highly heterogeneous operating conditions, while dynamically adapting to capacity and quality-of-service (QoS) demands, the sixth generation (6G) communication systems are required to undergo significant advances. Along with the proliferation of devices, a multitude of challenges remain towards aspired performance, versatility, security, and cost-effectiveness of next generation networks. The aim of this work is to identify said challenges and promising approaches, within the scenario of dense and complex urban deployments enabled by optical-wireless coexistence. Towards enabling such operation scenario, our 6G vision focuses on various aspects of four main innovation paradigms. First, as future systems target network capacities beyond the capabilities of current technologies, the scientific trend of pursuing radio frequencies edging towards the THz domain continues. To ensure reliable and efficient deployment and mobility under the cell densification paradigm, particularly in complex urban environments, novel approaches are required to address the challenges as latency, signal blockages, and unreliable handovers. Second, towards enabling efficient operation under the heterogeneous scenario, we highlight the cooperative coexistence paradigm as a key feature of the underlying physical layer architecture. Third, joint and holistic resource optimization is required towards dynamically optimized support of mm-wave and sub-THz operation while retaining legacy coexistence, where we consider features that may benefit from the research paths proposed within the second paradigm. Fourth, network security is identified as critical towards the market adoption of technologies proposed within the first paradigm, where we highlight aspects unique to our scenario and network vision. We provide a comprehensive overview of promising state-of-the-art approaches and identify relevant research gaps while holistically addressing the four aforementioned innovation paradigms.

Device-to-device (D2D) communications allows user equipment (UE) that are in close proximity to communicate with each other directly without using a base station. Relay-assisted D2D (RA-D2D) communications in 5G networks can be applied to support long-distance users and to improve energy efficiency (EE) of the networks. In this paper, we first establish a multi-relay system model where the D2D UEs can communicate with each other by reusing only one cellular uplink resource. Then, we apply an adaptive neuro-fuzzy inference system (ANFIS) architecture to select the best D2D relay to forward D2D source information to the expected D2D destination. Efficient power allocation (PA) in the D2D source and the D2D relay are critical problems for operating such networks, since the data rate of the cellular uplink and the maximum transmission power of the system need to be satisfied. As is known, 5G wireless networks also aim for low energy consumption to better implement the Internet of Things (IoT). Consequently, in this paper, we also formulate a problem to find the optimal solutions for PA of the D2D source and the D2D relay in terms of maximizing the EE of RA-D2D communications to support applications in the emerging IoT. To solve the PA problems of RA-D2D communications, a particle swarm optimization algorithm is employed to maximize the EE of the RA-D2D communications while satisfying the transmission power constraints of the D2D users, minimum data rate of cellular uplink, and minimum signal-to-interference-plus-noise-ratio requirements of the D2D users. Simulation results reveal that the proposed relay selection and PA methods significantly improve EE more than existing schemes.

Communication by jointly precoded transmission from many distributed access points (APs), called cell-free massive multiple-input multiple-output (CF mMIMO), is a promising concept for beyond 5G systems. One of the challenging aspects of CF mMIMO is the efficient management of the radio resources. We propose both reinforcement learning (RL)-based and heuristic precoding aware radio resource scheduling (RRS) algorithms aiming at maximizing sum spectral efficiency (SE). The proposed algorithms allocate resources for Maximum Ratio Transmission (MRT), Zero-Forcing (ZF), Regularised Zero-Forcing (RZF), and Optimized Zero Forcing (OZF) precoders. For the resource allocation, both the set of serving APs and the physical resource blocks are considered. In high noise scenarios, the proposed RL-based RRS algorithm combined with the MRT precoder shows 2.4 times higher sum SE than the standard Round Robin scheduler. Moreover, we demonstrate that the proposed heuristic algorithms offer similar sum SE while significantly reducing the complexity compared to the RL-based solution. We also show that the RZF and OZF precodings, which are superior to the ZF precoding in noisy environments, result overall in more transmitted power. Therefore, assuming the same radio resource schedule and precoding strategy in the neighboring cells, it will result in more inter-cell interference and an overall reduced performance.

Cooperative communication systems use cooperative relays for transmitting their data packets to the destination. Cooperative communication schemes having all relays participating in transmission may cause unnecessary wastes of most valuable spectrum resources. So it is mandatory to effectively select a transmission mode for cooperative cognitive radio networks (CCRNs). In this paper, an efficient transmission mode scheme based on Q-learning algorithm is proposed. State, action, and reward are defined to achieve a good performance on time delay and energy efficiency in data transmission as well as the interference to primary users during secondary users transmission. The proposed scheme selects an optimal action on the networks environment to maximize the total revenue of the multilateral metric. The simulation result shows that the proposed scheme can efficiently support the determination for the transmission mode and outperforms conventional schemes for a single metric in CCRNs.

Existing mobile communication systems are unable to support ultra high system capacity and high reliability for the edge users of future 6G systems, which are envisioned to guarantee the desired quality of experience. Recently, cell-less radio access networks (RAN) are exploited to boost the system capacity. Therefore, in this letter we propose a cell-less networking approach with an efficient radio resource optimization mechanism to improve the system capacity of the future 6G networks. The simulation results illustrate that the proposed cell-less NG-RAN design provides significant system capacity improvement over the legacy cellular solutions.