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With the increasing demand for wireless connectivity, ensuring the efficient coexistence of multiple radio access technologies in shared unlicensed spectrum has become an important issue. This paper focuses on optimizing Medium Access Control (MAC) parameters to enhance the coexistence of 5G New Radio in Unlicensed Spectrum (NR-U) and Wi-Fi networks operating in unlicensed spectrum with multiple priority classes of traffic that may have varying quality-of-service (QoS) requirements. In this context, we tackle the coexistence parameter management problem by introducing a QoS-aware State-Augmented Learnable (QaSAL) framework, designed to improve network performance under various traffic conditions. Our approach augments the state representation with constraint information, enabling dynamic policy adjustments to enforce QoS requirements effectively. Simulation results validate the effectiveness of QaSAL in managing NR-U and Wi-Fi coexistence, demonstrating improved channel access fairness while satisfying a latency constraint for high-priority traffic. 

We propose an energy-efficient power allocation algorithm for the multi-user millimeter-wave (mmWave) rate-splitting multiple access (RSMA) downlink with hybrid precoding and quality of service (QoS) constraints. The proposed scheme is applicable to the physical layer design of future wireless networks, such as the 6G cellular downlink, in which a transmitter equipped with multiple antennas must communicate unicast messages to multiple receivers simultaneously. First, we use a low-complexity design to define the analog and digital precoders in closed form. Second, we define an energy efficiency (EE) maximization problem to jointly optimize the power allocation among streams and the common stream rate allocation among users. We then solve the problem using a combination of Dinkelbach’s algorithm and difference of convex functions (DC) programming methods. Simulation results show that the proposed RSMA scheme offers EE improvements over a comparable space division multiple access (SDMA) power allocation scheme in scenarios with perfect and imperfect channel state information at the transmitter. Lastly, we present extensive numerical experiments that suggest that the computational complexity of the proposed RSMA energy-efficient power allocation algorithm can be reduced using the interior-point method such that the computational efficiency of RSMA is comparable to that of SDMA. 

Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks. 

We investigate the energy efficiency (EE) problem in a downlink multi-user millimeter wave (mmWave) rate-splitting multiple access (RSMA) system and propose an energy-efficient one-layer RSMA hybrid precoder design for K users with quality of service constraints. This scheme is applicable to the design of sustainable sixth generation (6G) cellular networks. To make the problem tractable, the analog and the digital precoder designs are decoupled. First, the analog precoder is designed to maximize the desired signal power of each user while ignoring multi-user interference. Second, the digital precoder is designed to manage multi-user interference according to the EE optimization design criterion. We adopt a successive convex approximation-based algorithm for joint optimization of the digital precoders, power, and common rate allocation. Simulation results show that the proposed RSMA scheme always performs at least as well as a baseline spatial division multiple access (SDMA) hybrid precoding scheme and outperforms it under certain channel conditions. These results suggest that RSMA is suitable as a flexible physical layer design for future 6G mmWave networks. 

This paper assesses the feasibility of a novel dynamic spectrum sharing approach for a cellular downlink based on cognitive overlay to allow non-orthogonal cellular transmissions from a primary and a secondary radio access technology concurrently on the same radio resources. The 2-user Gaussian cognitive interference channel is used to model a downlink scenario in which the primary and secondary base stations are co-located. A system architecture is defined that addresses practical challenges associated with cognitive overlay, in particular the noncausal knowledge of the primary user message at the cognitive transmitter. A cognitive overlay scheme is applied that combines superposition coding with dirty paper coding, and a primary user protection criterion is derived that is specific to a scenario in which the primary system is 4G while the secondary system is 5G. Simulation is used to evaluate the achievable signal-to-interference-plus-noise ratio (SINR) at the 4G and 5G receivers, as well as the cognitive power allocation parameter as a function of distance. Results suggest that the cognitive overlay scheme is feasible when the distance to the 5G receiver is relatively small, even when a large majority of the secondary user transmit power is allocated to protecting the primary user transmission. Achievable link distances for the 5G receiver are on the order of hundreds of meters for an urban macrocell or a few kilometers for a rural macrocell. 

In a wireless network with dynamic spectrum sharing, tracking temporal spectrum holes across a wide spectrum band is a challenging task. We consider a scenario in which the spectrum is divided into a large number of bands or channels, each of which has the potential to provide dynamic spectrum access opportunities. The occupancy times of each band by primary users are generally non-exponentially distributed. We develop an approach to determine and parameterize a small selected subset of the bands with good spectrum access opportunities, using limited computational resources under noisy measurements. We model the noisy measurements of the received signal in each band as a bivariate Markov modulated Gaussian process, which can be viewed as a continuous-time bivariate Markov chain observed through Gaussian noise. The underlying bivariate Markov process allows for the characterization of non-exponentially distributed state sojourn times. The proposed scheme combines an online expectation-maximization algorithm for parameter estimation with a computing budget allocation algorithm. Observation time is allocated across the bands to determine the subset of G out of G frequency bands with the largest mean idle times for dynamic spectrum access and at the same time to obtain accurate parameter estimates for this subset of bands. Our simulation results show that when channel holding times are non-exponential, the proposed scheme achieves a substantial improvement in the probability of correct selection of the best subset of bands compared to an approach based on a (univariate) Markov modulated Gaussian process model.