Dynamic multi-connectivity activation for ultra-reliable and low-latency communication

Abstract Multi-connectivity (MC) with packet duplication, where the same data packet is duplicated and transmitted from multiple transmitters, is proposed in 5G New Radio as a reliability enhancement feature. However, it is found to be resource inefficient, since radio resources from more than one transmitters are required to serve a single user. Improving the performance enhancement vs. resource utilization tradeoff with MC is therefore a key design challenge. This work proposes a heuristic resource efficient latency-aware dynamic MC algorithm which activates MC selectively such that its benefits are harnessed for critical users, while minimizing the corresponding resource usage. Numerical results indicate that the proposed algorithm can deliver the outage performance gains of legacy MC schemes while requiring up to 45% less resources

An analysis with interplay of NOMA and RSMA for RIS-aided system

Abstract Reconfigurable intelligent surface (RIS) has emerged as a potential technology for future-generation wireless communication by enhancing its signal quality and providing broader coverage network area. In this work, we provide an analytical framework of a RIS-assisted multi-user downlink system where the base station (BS) transmits a superimposed signal to multiple users with the aid of a RIS using non-orthogonal multiple access (NOMA) and rate splitting multiple access (RSMA) transmission technique. First, we discuss the statistical characteristics and evaluate the probability density function (PDF) of the different channels involved in the transmission. We then evaluate the system performance utilizing the PDF and obtain the analytical expressions of the outage probability through the application of NOMA and RSMA transmission techniques and verify the preciseness of the derived closed-form expressions using Monte-Carlo (MC) simulations. Moreover, to gain some useful insights about the system, we also highlight the impact of transmit power availability at the BS, imperfect channel state information (CSI) on the outage probability of each user, effect of number of RIS elements on outage probability. Lastly, we demonstrate the superiority of RSMA over NOMA on the performance of the system

Power-domain non-orthogonal multiple access based full-duplex one-way wireless relaying network

Abstract This study investigates power-domain non-orthogonal multiple access based wireless information exchange process. The investigation considers a dual-hop non-regenerative full-duplex wireless one-way relaying networks in the system model, where the source terminal transmits two different types of information and subtracts the interference signal at the destination by using successive interference cancellation technique. The outage probability, error probability, achievable rate, and ergodic rate of the considered system is analytically derived. In addition, optimum power allocation coefficients and relay terminal position are determined using the optimization techniques. Monte-Carlo simulation results validate the analytical and asymptotic derivations. The derived analytical expressions are found closely in agreement with the system level numerical results

Enabling URLLC for low-cost IoT devices via diversity combining schemes

Abstract Supporting Ultra-Reliable Low-Latency Communication (URLLC) in the Internet of Things (IoT) era is challenging due to stringent constraints on latency and reliability combined with the simple circuitry of IoT nodes. Diversity is usually required for sustaining the reliability levels of URLLC, but there is an additional delay associated to auxiliary procedures to be considered, specially when communication includes low-cost IoT devices. Herein, we analyze Selection Combining (SC) and Switch and Stay Combining (SSC) diversity schemes as plausible solutions for enabling ultra-reliable low-latency downlink communications to low-cost IoT devices. We demonstrate the necessity of considering the time spent in auxiliary procedures, which has not been traditionally taken into account, while we show its impact on the reliability performance. We show there is an optimum number of receive antennas, which suggests that under certain conditions it might be required to turn off some of them, specially under the SC operation. We highlight the superiority of SSC with respect to SC as long the associated Signal-to-Noise Ratio threshold is properly selected. We propose using a fixed threshold relying only on long-term channel fading statistics, which leads to near-optimum results

A nonlinear autoregressive neural network for interference prediction and resource allocation in URLLC scenarios

Abstract Ultra reliable low latency communications (URLLC) is a new service class introduced in 5G which is characterized by strict reliability (1–10 −5) and low latency requirements (1 ms). To meet these requisites, several strategies like overprovisioning of resources and channel-predictive algorithms have been developed. This paper describes the application of a Nonlinear Autoregressive Neural Network (NARNN) as a novel approach to forecast interference levels in a wireless system for the purpose of efficient resource allocation. Accurate interference forecasts also grant the possibility of meeting specific outage probability requirements in URLLC scenarios. Performance of this proposal is evaluated upon the basis of NARNN predictions accuracy and system resource usage. Our proposed approach achieved a promising mean absolute percentage error of 7.8 % on interference predictions and also reduced the resource usage in up to 15 % when compared to a recently proposed interference prediction algorithm

Average rate and error probability analysis in short packet communications over RIS-aided URLLC systems

Abstract In this paper, the average achievable rate and error probability of a reconfigurable intelligent surface (RIS) aided systems is investigated for the finite blocklength (FBL) regime. The performance loss due to the presence of phase errors arising from limited quantization levels as well as hardware impairments at the RIS elements is also discussed. First, the composite channel containing the direct path plus the product of reflected channels through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements, phase errors and the channels‘ path loss. Next, by considering the FBL regime, the achievable rate expression and error probability are identified and the corresponding average rate and average error probability are elaborated based on the proposed SNR distribution. Furthermore, the impact of the presence of phase error due to either limited quantization levels or hardware impairments on the average rate and error probability is discussed. The numerical results show that Monte Carlo simulations conform to matched Gamma distribution to received SNR for sufficiently large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable and low-latency systems. The difference between Shannon capacity and achievable rate in FBL regime is also discussed. Additionally, the required number of RIS elements to achieve a desired error probability in the FBL regime will be significantly reduced when the phase shifts are performed without error

A nonlinear autoregressive neural network for interference prediction and resource allocation in URLLC scenarios

Abstract Ultra reliable low latency communications (URLLC) is a new service class introduced in 5G which is characterized by strict reliability (1–10 −5) and low latency requirements (1 ms). To meet these requisites, several strategies like overprovisioning of resources and channel-predictive algorithms have been developed. This paper describes the application of a Nonlinear Autoregressive Neural Network (NARNN) as a novel approach to forecast interference levels in a wireless system for the purpose of efficient resource allocation. Accurate interference forecasts also grant the possibility of meeting specific outage probability requirements in URLLC scenarios. Performance of this proposal is evaluated upon the basis of NARNN predictions accuracy and system resource usage. Our proposed approach achieved a promising mean absolute percentage error of 7.8 % on interference predictions and also reduced the resource usage in up to 15 % when compared to a recently proposed interference prediction algorithm

Average rate analysis of RIS-aided short packet communication in URLLC systems

Abstract In this paper, the average achievable rate of a re- configurable intelligent surface (RIS) aided factory automation is investigated in finite blocklength (FBL) regime. First, the composite channel containing the direct path plus the product of reflected paths through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements as well as the channel pathloss. Next, by assuming FBL model, the achievable rate expression is identified and the corresponding average rate is elaborated based on the proposed SNR distribution. The phase error due to quantizing the phase shifts is considered in the simulation. The numerical results show that Monte Carlo simulations conform to the matched Gamma distribution for the received SNR for large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable low-latency communications (URLLC) systems. The reduction of average achievable rate due to working in FBL regime with respect to Shannon capacity is also investigated as a function of total RIS elements

Joint sum rate and blocklength optimization in RIS-aided short packet URLLC systems

Abstract In this paper, a multi-objective optimization problem (MOOP) is proposed for maximizing the achievable finite blocklength (FBL) rate while minimizing the utilized channel blocklengths (CBLs) in a reconfigurable intelligent surface (RIS)-assisted short packet communication system. The formulated MOOP has two objective functions namely maximizing the total FBL rate with a target error probability, and minimizing the total utilized CBLs which is directly proportional to the transmission duration. The considered MOOP variables are the base station (BS) transmit power, number of CBLs, and passive beamforming at the RIS. Since the proposed non-convex problem is intractable to solve, the Tchebyshev method is invoked to transform it into a single-objective OP, then the alternating optimization (AO) technique is employed to iteratively obtain optimized parameters in three main sub-problems. The numerical results show a fundamental trade-off between maximizing the achievable rate in the FBL regime and reducing the transmission duration. Also, the applicability of RIS technology is emphasized in reducing the utilized CBLs while increasing the achievable rate significantly

A predictive interference management algorithm for URLLC in beyond 5G networks

Abstract Interference mitigation is a major design challenge in wireless systems, especially in the context of ultra-reliable low-latency communication (URLLC) services. Conventional average-based interference management schemes are not suitable for URLLC as they do not accurately capture the tail information of the interference distribution. This letter proposes a novel interference prediction algorithm that considers the entire interference distribution instead of only the mean. The key idea is to model the interference variation as a discrete state space discrete-time Markov chain. The state transition probability matrix is used to estimate the state evolution in time, and allocate radio resources accordingly. The proposed scheme is found to meet the target reliability requirements in a low-latency single-shot transmission system considering realistic system assumptions, while requiring only ~ 25% more resources than the optimum case with perfect interference knowledge