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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. 

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 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. 

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. 

Stochastic network calculus involves the use of a traffic bound or envelope to make admission control and resource allocation decisions for providing end-to-end quality-of-service guarantees. To apply network calculus in practice, the traffic envelope should: (i) be readily determined for an arbitrary traffic source, (ii) be enforceable by traffic regulation, and (iii) yield statistical multiplexing gain. Existing traffic envelopes typically satisfy at most two of these properties. A well-known traffic envelope based on the moment generating function (MGF) of the arrival process satisfies only the third property. We propose a new traffic envelope based on the MGF of the workload process obtained from offering the traffic to a constant service rate queue. We show that this traffic workload envelope can achieve all three properties and leads to a framework for a network service that provides stochastic delay guarantees. We demonstrate the performance of the traffic workload envelope with two bursty traffic models: Markov on-off fluid and Markov modulated Poisson Process (MMPP). 

Network tomography aims at estimating source–destination traffic rates from link traffic measurements. This inverse problem was formulated by Vardi in 1996 for Poisson traffic over networks operating under deterministic as well as random routing regimes. In this article, we expand Vardi's second-order moment matching rate estimation approach to higher-order cumulant matching with the goal of increasing the column rank of the mapping and consequently improving the rate estimation accuracy. We develop a systematic set of linear cumulant matching equations and express them compactly in terms of the Khatri–Rao product. Both least squares estimation and iterative minimum I-divergence estimation are considered. We develop an upper bound on the mean squared error (MSE) in least squares rate estimation from empirical cumulants. We demonstrate that supplementing Vardi's approach with the third-order empirical cumulant reduces its minimum averaged normalized MSE in rate estimation by almost 20% when iterative minimum I-divergence estimation was used. 

Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Since deterministic bounds are based on the worst-case traffic behavior, various frameworks for stochastic network calculus have been proposed to provide less conservative, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We design and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Two alternative implementations of the stochastic regulator are developed and compared. Numerical results are provided to demonstrate the performance of the proposed stochastic traffic regulator. 

Network tomography aims at estimating source-destination traffic rates from link traffic measurements. This inverse problem was formulated by Vardi in 1996 for independent Poisson traffic over networks operating under deterministic as well as random routing regimes. Vardi used a second-order moment matching approach to estimate the rates where a solution for the resulting linear matrix equation was obtained using an iterative minimum I-divergence procedure. Vardi’s second-order moment matching approach was recently extended to higher order cumulant matching approach with the goal of improving the rank of the system of linear equations. In this paper we go one step further and develop a moment generating function matching approach for rate estimation, and seek a least squares as well as an iterative minimum I-divergence solution of the resulting linear equations. We also specialize this approach to a characteristic function matching approach which exhibits some advantages. These follow from the fact that the characteristic function matching approach results in fewer conflicting equations involving the empirical estimates. We demonstrate that the new approach outperforms the cumulant matching approach while being conceptually simpler. 

The provisioning of delay guarantees in packet-switched networks such as the Internet remains an important, yet challenging open problem. We propose and evaluate a framework, based on results from stochastic network calculus, for guaranteeing stochastic bounds on network delay at a statistical multiplexer. The framework consists of phase-type traffic bounds and moment generating function traffic envelopes, stochastic traffic regulators to enforce the traffic bounds, and an admission control scheme to ensure that a stochastic delay bound is maintained for a given set of flows. Through numerical examples, we show that a stochastic delay bound is maintained at the multiplexer, and contrast the proposed framework to an approach based on deterministic network calculus. 

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.