Performance Modeling and Stability of Semi-Persistent Scheduling with Initial Random Access in LTE

Seo, Jun-Bae; Leung, Victor C. M.

doi:10.1109/twc.2012.100112.112254

Cited by 38 publications

(21 citation statements)

References 12 publications

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“…UE j can either receive data directly over the B2D link (direct mode) 2 In LTE networks, resources are divided into Resource Blocks, which consist of 12 consecutive 15 kHz subcarriers (a total of 180 kHz) for the duration of one slot (0.5 ms). The smallest schedulable block is two consecutive resource blocks lasting the duration of one subframe (1 ms); however, scheduling can be done less frequently using semi-persistent scheduling, e.g., every frame (10 ms) or over multiple frames [36]. To mirror the LTE physical layer, we assume that UEs at the same base station are scheduled over orthogonal time-frequency resources.…”

Section: Direct and Relay-assisted Transmission Ratesmentioning

confidence: 99%

To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks

Mastronarde

Patel

et al. 2016

IEEE Trans. on Mobile Comput.

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We consider a cellular network where mobile transceiver devices that are owned by self-interested users are incentivized to cooperate with each other using tokens, which they exchange electronically to "buy" and "sell" downlink relay services, thereby increasing the network's capacity compared to a network that only supports base station-to-device (B2D) communications. We investigate how an individual device in the network can learn its optimal cooperation policy online, which it uses to decide whether or not to provide downlink relay services for other devices in exchange for tokens. We propose a supervised learning algorithm that devices can deploy to learn their optimal cooperation strategies online given their experienced network environment. We then systematically evaluate the learning algorithm in various deployment scenarios. Our simulation results suggest that devices have the greatest incentive to cooperate when the network contains (i) many devices with high energy budgets for relaying, (ii) many highly mobile users (e.g., users in motor vehicles), and (iii) neither too few nor too many tokens. Additionally, within the token system, self-interested devices can effectively learn to cooperate online, and achieve up to 20% higher throughput on average than with B2D communications alone, all while selfishly maximizing their own utilities.

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Section: Direct and Relay-assisted Transmission Ratesmentioning

confidence: 99%

To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks

Mastronarde

Patel

et al. 2016

IEEE Trans. on Mobile Comput.

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“…In addition, random access recently re-gained interest due to the increased number of communicating devices in 5G networks, and the need for massive uncoordinated access [18]. Random access and alternatives schemes and their effect on the operation of LTE and LTE-A are presented in [18], [19], [20]. In [21], the effect of random access in Cloud-Radio Access Network is considered.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of a cooperative wireless network with adaptive relays

Dimitriou

Παππάς

2019

Ad Hoc Networks

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In this work, we investigate a slotted-time relay assisted cooperative random access wireless network with multipacket (MPR) reception capabilities. MPR refers to the capability of a wireless node to successfully receive packets from more than two other modes that transmit simultaneously at the same slot. We consider a network of N saturated sources that transmit packets to a common destination node with the cooperation of two infinite capacity relay nodes. The relays assist the sources by forwarding the packets that failed to reach the destination. Moreover, the relays have also packets of their own to transmit to the destination. We further assume that the relays employ a state-dependent retransmission control mechanism. In particular, a relay node accordingly adapts its transmission probability based on the status of the other relay. Such a protocol is towards self-aware networks and leads to substantial performance gains in terms of delay. We investigate the stability region and the throughput performance for the full MPR model. Moreover, for the asymmetric two-sources, two-relay case we derive the generating function of the stationary joint queuelength distribution with the aid of the theory of boundary value problems. For Email addresses: idimit@math.upatras.gr (Ioannis Dimitriou), nikolaos.pappas@liu.se (Nikolaos Pappas)the symmetric case, we obtain explicit expressions for the average queueing delay in a relay node without solving a boundary value problem. Extensive numerical examples are presented and provide insights on the system performance.

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“…In this section, we will review four potential UL scheduling mechanisms, based on existing scheduling in the LTE, but also potential proposals for ultra-low latency applications. These four scheduling mechanisms are chosen based on the literature ( [2], [6], [7], [8], and [9]) in a way that they cover a diverse range of UL scheduling techniques in terms of their offered delay and communication resource consumption. The main goal of this section is to summarize delay incurred by each of these UL scheduling techniques.…”

Section: Uplink Scheduling Mechanismsmentioning

confidence: 99%

“…This mechanism, referred to in the literature as Semi-Persistent Scheduling (SPS), is an effective way to reduce scheduling latency of deterministic traffic like VoIP, but it will have inverse effect on the spectral efficiency in the case of bursty traffic due to un-used allocated resource in non-burst periods. Also, SPS might fail to transmit all packets during burst periods [8].…”

Section: B Semi-persistent Schedulingmentioning

confidence: 99%

Analysis of Uplink Scheduling for Haptic Communications

Mahlouji

Mahmoodi

2018

2018 IEEE Globecom Workshops (GC Wkshps)

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While new mechanisms and configurations of the 5G radio are offering step forward in delivery of ultra-reliable low latency communication services in general, and haptic communications in particular, they could inversely impact the remainder of traffic services. In this paper, we investigate the uplink access procedure, how different advances in this procedure enhance delivery of haptic communication, and how it affects the remainder of traffic services in the network. We model this impact as the remainder of service, using stochastic network calculus. Our results show how best the tradeoff between faster or more resource efficient uplink access can be made depending on the rate of haptic data, which is directly relevant to the application domain of haptic communication.

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Performance Modeling and Stability of Semi-Persistent Scheduling with Initial Random Access in LTE

Cited by 38 publications

References 12 publications

To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks

To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks

Performance analysis of a cooperative wireless network with adaptive relays

Analysis of Uplink Scheduling for Haptic Communications

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