We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multi-connectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can form multiple clusters. The robots in a cluster are close to each other and can have reliable Device-to-Device (D2D) communications. In Phase I, the APs transmit the combined payload of a cluster to the cluster leader within a latency constraint. The cluster leader broadcasts this message to its members in Phase II. We develop a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for joint user association and resource allocation (RA) for Phase I. The cluster leaders use their local Channel State Information (CSI) to decide the APs for connection along with the sub-band and power level. The cluster leaders utilize multi-connectivity to connect to multiple APs to increase their reliability. The objective is to maximize the successful payload delivery probability for all robots. Illustrative simulation results indicate that the proposed scheme can approach the performance of the centralized algorithm and offer a substantial gain in reliability as compared to single-connectivity (when cluster leaders are able to connect to 1 AP).

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of high user mobility in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, acting as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.

We consider the problem of joint user association, channel assignment, and power allocation for mobile robot application in factory automation system that require ultrareliable and low latency communications (URLLC). The aim is to deliver control commands from the controller to mobile robots with stringent requirements of latency and reliability. To achieve URLLC, we develop a two-phase communication scheme. The robots work close to each other in a factory environment and can form clusters for reliable device-to-device (D2D) communications. Within the latency requirements, the combined payload of a cluster is transmitted to the leader in Phase I. In Phase II, the leader broadcasts the payload to its members. Under this strategy, we use multi-agent reinforcement learning (MARL) for resource allocation. The cluster leaders in Phase I act as the agents and interact with the environment to optimally select the Access Point (AP) for connection along with the sub-band and power level. The objective is to maximize the successful payload delivery probability to all the robots. Illustrative simulation results indicate that the proposed scheme can offer average successful payload delivery probability close to that of centralized exhaustive search algorithm.

This paper studies the benefits of incorporating underlaid full-duplex (FD) device-to-device (D2D) communications into massive multiple-input-multiple-output (MIMO) downlink systems. Due to the nature of cellular downlink and FD D2D transmission, the performances of cellular and D2D services are severely impaired due to high interference caused by base-stations (BSs) and D2D transceivers. As a consequence, integrating a large number of D2D links into existing cellular networks might degrade the system performance. To overcome this challenge, utilizing the large uniform linear array (ULA) equipped at BSs, we propose a joint beamforming and power allocation design for average sum-rate maximization while considering the effects of interference to both cellular and D2D transmissions. The problem formulation leads to a nonconvex vector-variable optimization problem, where we develop an efficient solution using a fractional programming (FP) based approach. Numerical results show that, at sufficiently high self-interference cancellation (SIC) levels and numbers of active D2D links, the FD D2D transmission provides a significant sum-rate improvement as compared to the half-duplex (HD) counterpart and pure cellular systems in absence of D2D.

This paper utilizes the tools of stochastic geometry to derive closed-form approximations of coverage probabilities for both cellular and D2D links of a realistic underlaid full-duplex (FD) cellular D2D network. In the considered model, D2D users operate in FD mode under the adverse effect of realistic residual self-interference, and their locations are modeled by a homogeneous spatial Poison point process (PPP). The coverage probabilities involve multiple integrals due to the average over the distributions of transmit power, wireless fading, and link distances. Therefore, our solution is to first apply Laplace transforms and novel approximations that accurately approximate the expected values of fractional and exponential functions of random variables to obtain the distributions of signal to interference and noise ratios (SINRs) at the base station (BS) and D2D users. By further taking the average over the distributions of cellular and D2D link distances, we then arrive at the closed-form approximations of cellular and D2D coverage probabilities. Furthermore, based on the approximation of D2D link coverage probability, an analytical expression of the D2D link sum-rate is obtained, which can be effectively calculated. Our results show that the integration of FD in D2D provides significant sum-rate improvement over the half-duplex D2D counterpart.

This paper details novel methods to accurately estimate the achievable rates of channels with additive Gaussian mixture (GM) noise. Attention is paid to a Gaussian input and discrete inputs. Such discrete inputs represent a wide range of signaling strategies and include the capacity-achieving input as a special case. At first, we propose a simple technique to calculate the GM noise entropy. Specifically, when the noise level is high, a lower bound on the integrand of the noise entropy is established and the noise entropy can be estimated in closed-form. In the low noise region, the piecewise-linear curve fitting (PWLCF) method is applied to calculate the noise entropy. It is then demonstrated this can be estimated in both regions with a predetermined accuracy. We then extend this result to calculate the output entropy and the achievable rate when the input is Gaussian distributed, which is shown to be asymptotically optimal. Next, we propose a simple PWLCF-based method to estimate the output entropy for a given discrete input. In particular, the output entropy is evaluated by examining the output in high and low regions of amplitude using a lower bound on the integrand of the output entropy and PWLCF, respectively. It is demonstrated that the output entropy, and consequently, the achievable rates, can be computed to achieve any desired accuracy level.

In this paper, we detail effective methods to approximate the achievable rates of channels with additive Gaussian mixture (GM) noise for both real and complex channels to achieve any desired level of accuracy. Attention is paid to a Gaussian input, a discrete real input, and a complex input with discrete amplitude and independent uniform phase. Such discrete inputs represent a wide range of input distributions and they include the capacity-achieving inputs as special cases. At first, we propose a simple technique to accurately calculate the noise entropy. Specifically, when the noise level is high, a lower bound on the integrand of the entropy is established and the noise entropy can be estimated using a closed-form solution. In the low noise region, the piecewise-linear curve fitting (PWLCF) method is applied. We then extend this result to calculate the achievable rate when the input is Gaussian distributed, which is shown to be asymptotically optimal. Next, we propose a simple PWLCF-based method to approximate the output entropy for a real GM channel when the input is discrete, and for a complex GM channel when the input is discrete in amplitude with independent uniform phase. In particular, for the real channel, the output entropy is evaluated by examining the output in high and low regions of amplitude using a lower bound on the integrand of the output entropy and PWLCF, respectively. For the complex channel, the output entropy is approximated a similar manner but using polar coordinates and the Kernel function. It is demonstrated that the output entropy, and consequently, the achievable rates, can be computed to achieve any given accuracy level.

This paper studies the characterization of the optimal input for impulsive ambient noise channels under average power constraint. Our focus is on the two-term Gaussian mixture complex noise model, which has been widely used to model impulsive noise arising in various communication channels. We first demonstrate that there exists a unique input distribution that achieves the channel capacity and the capacity-achieving input distribution has a uniformly distributed phase. By examining the Kuhn-Tucker conditions (KTC), we further show that if the optimal amplitude input distribution contains an infinite number of mass points on a bounded interval, the channel output must be Gaussian distributed. However, by using Bernstein's theorem to examine the completely monotonic condition, it is shown that the assumption of a Gaussian distributed output is not valid. As a result, there is always a finite number of mass points on any bounded interval in the optimal amplitude distribution. In addition, by applying a novel bounding technique on the KTC and using the Envelop Theorem, we demonstrate that the optimal amplitude distribution cannot have an infinite number of mass points. That gives us a unique solution of the optimal input having discrete amplitude with a finite number of mass points. Given such interesting results, we also develop an efficient way to compute the discrete optimal input and the corresponding capacity.

This paper studies the characterization of the optimal input and the computation of the capacity of additive quadrature Gaussian mixture (GM) noise channels under an average power constraint. The considered model can be used to represent a wide variety of channels with impulsive interference, such as the well-known Bernoulli-Gaussian and Middleton class-A impulsive noise channels, as well as multiple-access interference channels and cognitive radio channels under imperfect sensing. At first, we demonstrate that there exists a unique input distribution that achieves the channel capacity, and the capacity-achieving input distribution has a uniformly distributed phase. By examining the Kuhn-Tucker alignment conditions (KTCs), we further show that, if the optimal input amplitude distribution contains an infinite number of mass points on a bounded interval, the channel output must be Gaussian-distributed. However, by using Bernstein's theorem to examine the completely monotonic condition, it is shown that the assumption of a Gaussian-distributed output is not valid. As a result, there are always a finite number of mass points on any bounded interval in the optimal amplitude distribution. In addition, by applying a novel bounding technique on the KTC and using the envelop theorem, we demonstrate that the optimal amplitude distribution cannot have an infinite number of mass points. This gives us the unique solution of the optimal input having discrete amplitude with a finite number of mass points. Given this discrete nature of the optimal input, we then develop a simple method to compute the discrete optimal input and the corresponding capacity. Our numerical examples show that, in many cases, the capacity-achieving distribution consists of only one or two mass points.

This paper presents simple methods to tightly estimate the information rate achieved by a Gaussian input and the constrained capacity of a finite-alphabet input of a Bernoulli-Gaussian (BG) impulsive noise channel in Rayleigh fading. Specifically, under the assumption of a Gaussian input, we propose a novel approach to calculate the achievable rate by examining the instantaneous output entropy in two regions of channel gains. In the high-gain region, the rate is evaluated via an upper bound obtained under the Gaussian output assumption. In the other region, we apply the piecewise-linear curve fitting (PWLCF) method to estimate the rate. It is then demonstrated that the information rate achieved by Gaussian inputs can be effectively calculated with a pre-determined accuracy. For a finite-alphabet input, we detail a PWLCF-based method to estimate the constrained capacity. In particular, we first propose a numerical technique to calculate the instantaneous output entropy using 2-dimensional Gauss-Hermite quadrature formulas. The average output entropy is then obtained using PWLCF. Combined with the closed-form expression the entropy of the BG impulse noise, an accurate estimation of the constrained capacity is finally established.

This paper presents a novel approach to tightly estimate the ergodic Shannon and constrained capacities of an additive Bernoulli-Gaussian (BG) impulsive noise channel in Rayleigh fading environments where channel gains are known at the receiver, but not at the transmitter. We first show that the differential entropy of the BG impulsive noise can be established in closed-form using Gaussian hypergeometric function2F1(1, 1; ·; ·). The Shannon capacity is then calculated via upper and lower bounds. Specifically, we derive in closed-form two upper bounds on the Shannon capacity using the assumption of a Gaussian output and using full knowledge of noise state, respectively. Under the assumption of a Gaussian input, we propose a novel approach to calculate a lower bound by examining the instantaneous output entropy in two regions of channel gains. In the high-gain region, the lower bound is evaluated via the upper bound obtained under the Gaussian output assumption. In the other region, we apply the piecewise-linear curve fitting (PWLCF) method to estimate the lower bound. It is then demonstrated that the lower bound can be calculated with a predetermined accuracy. By establishing the difference between the lower bound and the two upper bounds, we show that the lower bound can be used to effectively estimate the Shannon capacity. Finally, we detail a PWLCF-based method to estimate the constrained capacity for a finite-alphabet constellation. To this end, we first propose a numerical technique to calculate the instantaneous entropy of the output using 2-dimensional (2-D) Gauss-Hermite quadrature formulas. The average output entropy is then obtained using the PWLCF method. Combined with the closed-form expression of the entropy of the BG impulsive noise, the constrained capacity can be effectively estimated.

In this paper, we investigate the channel capacity of an additive Bernoulli-Gaussian (BG) impulsive noise channel in Rayleigh fading via lower and upper bounds. To this end, we first show that the differential entropy of the BG impulse noise can be established in closed-form using Gaussian hypergeometric function2F1(1, 1; .; .). This closed-form expression allows us to derive a lower bound on the capacity limit obtained by a Gaussian input using the Gauss-Hermite quadrature formula. We also derive in closed-form two upper bounds on the channel capacity. The first upper bound is obtained under the assumption of full knowledge of noise state, while the second upper bound is developed using a Gaussian distributed output. At high power regions, the lower bound achieved by Gaussian inputs and the upper bound generated by Gaussian inputs are indistinguishable. These two bounds can therefore be used as an accurate estimation for the channel capacity. When the channel input power is small compared to the power of the impulsive noise component, the lower bound obtained by using a Gaussian input and the upper bound under the perfect knowledge of impulse noise state are almost identical, which are useful to predict the capacity. The establishment of the lower bound and the two upper bounds in closed-form helps us to confirm the near-optimality of the Gaussian input in a wide range of input power levels over BG impulsive noise channels in Rayleigh fading.