We consider the joint scheduling of uplink URLLC and eMBB user traffic in a cellular system. The central challenge is coordinating URLLC uplink user transmissions for traffic requiring extremely low latency, high reliability, and in the absence of knowledge of instantaneous URLLC channel qualities, albeit with knowledge of channel distributions. To avoid collisions and meet latency and reliability constraints, we propose to pre optimize layouts of non-overlapping transmission opportunities for URLLC users’, which may, or may not, be used depending on their traffic. To increase overall throughput we propose to leverage Non-Orthogonal Multiple Access (NOMA) based opportunistic scheduling of overlapping eMBB user traffic and propose power control policies, i.e. eMBB transmit power backoff, to protect possible URLLC transmissions from overlapping eMBB traffic. We derive the sum-rate optimal power control for eMBB traffic, and propose a linear approximation that simplifies the later scheduling task. Utilizing an outage capacity model for the unknown URLLC channel qualities, we assign the URLLC allocations as a greedy first fit decreasing packing problem. We then apply an opportunistic overlapping scheduler that, subject to meeting URLLC users' latency constraints, optimizes eMBB users’ sum utility. Substantial discrete event simulations were conducted to explore the performance impact of system parameters associated with URLLC traffic requirements, eMBB power control, etc. Depending on the traffic scenarios, we show gains reaching 75% in the sum eMBB throughput and/or 5th percentile throughput relative to an orthogonal multiple access baseline.

Emerging 5G systems will need to efficiently support both broadband traffic (eMBB) and ultra-low-latency (URLLC) traffic. In these systems, time is divided into slots which are further sub-divided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic. We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while satisfying instantaneous URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocations decisions of the eMBB scheduler. Next we consider a more general class of (convex) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but time-homogeneous policies. We validate the characteristics and benefits of our schedulers via simulation.

5G wireless networks are expected to support ultra-reliable low latency communications (URLLC) traffic which requires very low packet delays (<; 1 ms) and extremely high reliability (~99.999%). In this paper, we focus on the design of a wireless system supporting downlink URLLC traffic. Using a queuing network-based model for the wireless system, we characterize the effect of various design choices on the maximum URLLC load it can support, including: 1) system parameters such as the bandwidth, link SINR, and QoS requirements; 2) resource allocation schemes in orthogonal frequency-division multiple access (OFDMA)-based systems; and 3) hybrid automatic repeat request schemes. Key contributions of this paper which are of practical interest are: 1) study of how the minimum required system bandwidth to support a given URLLC load scales with associated QoS constraints; 2) characterization of optimal OFDMA resource allocation schemes which maximize the admissible URLLC load; and 3) optimization of a repetition code-based packet re-transmission scheme.

Traditional wireless schedulers have been driven by rate-based criteria, e.g., utility maximizing/proportionally fair, and/or queue-based packet schedulers which do not directly reflect the Quality of Experience (QoE) associated with flow-based transactions and services. This paper proposes, a Measurement-Based Delay Optimal (MBDO) scheduler, which optimizes a cost function of the mean flow delays in a multi-class system, e.g,. web interactive, file downloads, etc. In this context the cost function expresses desired trade-offs amongst traffic classes reflecting heterogeneous QoE sensitivities which are nonlinear in the flow delays and/or system loads. To achieve optimality, MBDO scheduling uses measured system variables and knowledge (or measurement) of class flow-size distributions to adapt a weighted Gittins index scheduler. We show that under mild assumptions, and in a stationary regime, that MBDO scheduling is indeed asymptotically optimal. Perhaps more importantly, MBDO schedulers can self-optimize by adapting to slowly varying traffic loads, mixes and flow size distributions. Our extensive simulations confirm the effectiveness at realizing trade-offs and performance of the proposed approach.

Modern broadband wireless networks support application mixes, with different, possibly complex, application/user Quality of Service/Experience (QoS/QoE) metrics. The central problem underlying resource allocation for such systems is realizing QoS/QoE trade-offs given the dynamic loads and capacity variability they would typically see. The paper explores a framework for context-aware scheduling based on: (1) context-aware flow classification and management, and (2), complementary base station scheduler. Motivated by typical flow-size distributions for current traffic and characteristics of the associated delay optimal (Gittins index-based) schedulers we propose a novel flow and channel-aware scheduler which meets our design objectives. Using a combination of analysis and simulation we explore the achieved QoS/QoE trade-offs across a dynamic mix of traffic, in particular: 1) mobile web browsing and small file delays; 2) stored streaming video quality vs re-buffering; 3) throughput of larger file downloads. They suggest improved QoS/QoE tradeoffs vs traditional proportionally fair schedulers and are robust to the network load.

In several wireless sensor networks, it is of interest to determine the maximum of the sensor readings and identify the sensor responsible for it. We propose a novel, decentralized, scalable, energy-efficient, timer-based, one-shot max function computation (TMC) algorithm. In it, the sensor nodes do not transmit their readings in a centrally pre-defined sequence. Instead, the nodes are grouped into clusters, and computation occurs over two contention stages. First, the nodes in each cluster contend with each other using the timer scheme to transmit their reading to their cluster-heads. Thereafter, the cluster-heads use the timer scheme to transmit the highest sensor reading in their cluster to the fusion node. One new challenge is that the use of the timer scheme leads to collisions, which can make the algorithm fail. We optimize the algorithm to minimize the average time required to determine the maximum subject to a constraint on the probability that it fails to find the maximum. TMC significantly lowers average function computation time, average number of transmissions, and average energy consumption compared to approaches proposed in the literature.

In several wireless sensor networks, it is of interest to determine the maximum of the sensor readings and identify the sensor responsible for it. This has been referred to as the max function computation problem in the literature. We propose a novel, decentralized, timer-based max function computation (TMC) algorithm. In it, the sensors do not transmit their readings in a centrally pre-defined sequence. Instead, they are divided into clusters. The computation occurs over two stages. In the first stage, the nodes contend with each other using a decentralized timer scheme to transmit their reading to their cluster heads. In the second stage, the cluster heads contend in a similar manner. The main challenge that arises with the use of the timer scheme is the possibility of collisions, which can make the algorithm fail in finding the maximum. We optimize the algorithm to minimize the average time required to determine the maximum subject to a constraint on the probability that it fails to find it due to collisions. Extensive benchmarking shows that TMC requires lower selection times and far fewer transmissions on average than other approaches proposed in the literature.