Joint Uplink and Downlink Resource Configuration for Ultra-Reliable and Low-Latency Communications

She, Changyang; Yang, Chenyang; Quek, Tony Q. S.

doi:10.1109/tcomm.2018.2791598

Cited by 125 publications

(77 citation statements)

References 34 publications

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“…3(b) can be used, where packets of different users stay in one queue. As proved in [9], if the arrival process of each user follows Poisson process and the packet size are identical (e.g., each short packet in URLLC contains 20 bytes [1]), then the statistical multiplexing server can guarantee the QoS of all the packets with less total bandwidth compared with that adopting individual server. In practical systems, the transmission/processing time of different kinds of packets can be very diverse.…”

Section: B DL Scheduling Policies In Communication and Computing Sysmentioning

confidence: 99%

Toward Ultrareliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Feng

She

Ying³

et al. 2019

IEEE Veh. Technol. Mag.

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Ultra-reliable low-latency communications (URLLC) has been considered as one of the three new application scenarios in the 5th Generation (5G) New Radio (NR), where the physical layer design aspects have been specified. With the 5G NR, we can guarantee the reliability and latency in radio access networks. However, for communication scenarios where the transmission involves both radio access and wide area core networks, the delay in radio access networks only contributes to part of the end-to-end (E2E) delay. In this paper, we outline the delay components and packet loss probabilities in typical communication scenarios of URLLC, and formulate the constraints on E2E delay and overall packet loss probability. Then, we summarize possible solutions in the physical layer, the link layer, the network layer, and the cross-layer design, respectively. Finally, we discuss the open issues in prediction and communication co-design for URLLC in wide area large scale networks. * Accepted by IEEE Vehicular Technology Magazine, 2019. example, in LTE systems, the control signaling for uplink (UL) scheduling leads to a high latency that is much longer than 1 ms in the control plane [3]. Thus, how to design grant-free access techniques for URLLC deserves further study. Besides, with the first-come-first-serve (FCFS) scheduling policy, the short packets of URLLC services may need to wait for the processing of long packets of eMBB services. Thus, FCFS policy may not be the optimal policy for short packets in URLLC services, and other policies should be considered to minimize the E2E delay.The current techniques in the 5G NR [4] mainly focus on achieving the target E2E performance in local area communications, where all the user equipment (UE) lies in one or few adjacent cells. For different communication scenarios, the network architectures are different. In factory automation, the communication area is limited in a smart factory, while for remote control, the controller and slave can be located on different continents. As a result, the latency in radio access network only contributes a small portion of the E2E delay, and other delay components such as core network delay over a long distance large scale network and processing delay in the computing systems may be the dominant components [5]. Therefore, how to improve the E2E performance with different network architectures is still a challenging issue.In this paper, we focus on how to guarantee the E2E delay and overall packet loss probability in different communication scenarios, including local area communications, mobile edge computing (MEC) systems, and the long distance large scale networks. The rest of this paper is organized as follows:• We elaborate possible components of the E2E delay and overall packet loss probability in typical communication scenarios, and provide a general way to formulate the quality-of-service (QoS) constraints of URLLC.• We summarize possible solutions and techniques in physical layer, link layer, network layer, and cross-layer design aspects for URLLC...

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Section: B DL Scheduling Policies In Communication and Computing Sysmentioning

confidence: 99%

Toward Ultrareliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Feng

She

Ying³

et al. 2019

IEEE Veh. Technol. Mag.

Self Cite

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“…• we propose a joint power control and rate allocation 2 Notice that some URLLC applications, e.g., tactile Internet, may require the joint design of downlink and uplink communications (check for instance [26]). Such analysis is out of the scope of this paper; however, as future work we intend to extend our results for the Multiple-Input Multiple-Output (MIMO) scenario, while considering the mentioned joint downlink and uplink design.…”

Section: And/or If Power Consumptionmentioning

confidence: 99%

“…be able to estimate the CSI. Notice that this overhead can be accounted as part of the constraint r min (check Section III); while although we assume perfect CSI, the imperfectness may be modeled as a loss in the SIR as in [12], [26]. 4 Diversity is an important building block for supporting URLLC [3], and herein we focus simply on spatial diversity taking advantage of the multiple receive antennas.…”

Section: System Modelmentioning

confidence: 99%

Joint Power Control and Rate Allocation Enabling Ultra-Reliability and Energy Efficiency in SIMO Wireless Networks

López

Alves

2019

IEEE Trans. Commun.

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Coming cellular systems are envisioned to open up to new services with stringent reliability and energy efficiency requirements. In this paper we focus on the joint power control and rate allocation problem in Single-Input Multiple-Output (SIMO) wireless systems with Rayleigh fading and stringent reliability constraints. We propose an allocation scheme that maximizes the energy efficiency of the system while making use only of average statistics of the signal and interference, and the number of antennas M that are available at the receiver side. We show the superiority of the Maximum Ratio Combining (MRC) scheme over Selection Combining (SC) in terms of energy efficiency, and prove that the gap between the optimum allocated resources converges to (M !) 1/(2M ) as the reliability constraint becomes more stringent. Meanwhile, in most cases MRC was also shown to be more energy efficient than Switch and Stay Combining (SSC) scheme, although this does not hold only when operating with extremely large M , extremely high/small average signal/interference power and/or highly power consuming receiving circuitry. Numerical results show the feasibility of the ultra-reliable operation when M increases, while greater the fixed power consumption and/or drain efficiency of the transmit amplifier is, the greater the optimum transmit power and rate.

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“…Note that the decoding error probability requirement has not been considered in [23] and [24], and the throughput is less important in URRLC as only control signals or measurement data with small packet size are transmitted in URLLC. In [25], She et al jointly optimized the uplink and downlink transmission blocklengths to minimize the required total bandiwidth based on statistical channel state information (CSI). However, the optimization is based on the simplified expression of the rate for SPT, which cannot accurately characterize the relationship between the decoding error probability and blocklength.…”

Section: Introductionmentioning

confidence: 99%

Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario

Ren

Pan

Deng

et al. 2020

IEEE Trans. Wireless Commun.

149

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Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10 −9 packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon's capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.

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Joint Uplink and Downlink Resource Configuration for Ultra-Reliable and Low-Latency Communications

Cited by 125 publications

References 34 publications

Toward Ultrareliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Toward Ultrareliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Joint Power Control and Rate Allocation Enabling Ultra-Reliability and Energy Efficiency in SIMO Wireless Networks

Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario

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