A Delay Efficient MAC and Packet Scheduler for Heterogeneous M2M Uplink

Abstract

The uplink data arriving at the Machine-to-Machine (M2M) Application Server (AS) via M2M Aggregators (MAs) is fairly heterogeneous along several dimensions such as maximum tolerable packet delay, payload size and arrival rate, thus necessitating the design of Quality-of-Service (QoS) aware packet scheduler. In this paper, we classify the M2M uplink data into multiple QoS classes and use sigmoidal function to map the delay requirements of each class onto utility functions. We propose a proportionally fair delay-optimal multiclass packet scheduler at AS that maximizes a system utility metric. We note that the average class delay under any work-conserving scheduling policy can be realized by appropriately time-sharing between all possible preemptive priority policies. Therefore the optimal scheduler is determined using an iterative process to determine the optimal time-sharing between all priority scheduling policies, such that it results in maximum system utility. The proposed scheduler can be implemented online with reduced complexity due to the iterative optimization process. We then extend this work to determine jointly optimal MA-AS channel allocation and packet scheduling scheme at the MAs and AS. We first formulate a joint optimization problem that is solved centrally at the AS and then propose a low complexity distributed optimization problem solved independently at MAs and AS. We show that the distributed optimization solution converges quickly to the centralized optimization result with minimal information exchange overhead between MAs and AS. Using Monte-Carlo simulations, we verify the optimality of the proposed scheduler and show that it outperforms other state-of-the-art packet schedulers such as weighted round robin, max-weight scheduler etc. Another desirable feature of proposed scheduler is low delay jitter for delay-sensitive traffic.

Massive Machine Type Communication With Data Aggregation and Resource Scheduling

“…Letm denote the average number of MTDs in each aggregator's serving zone. The density of MTDs is then given by λ m =mλ a [22], and the instantaneous number of MTDs in each aggregator, denoted as K, follows the Poisson distribution with meanm, i.e., Pr(K = k) = 1 k!m k exp(−m) [21].…”

“…The analytical framework developed in this paper can be applied to the network model in [15]. 2 Similar to [11], [15], [18], [20], [21], [25], we do not model the random access in the network and the MTDs considered in the aggregation phase can be viewed as the MTDs that have been granted access to the aggregators. 3 For simplicity, we assume that each aggregator has no buffer and transmits all its aggregated data in one go.…”

“…In [20], the authors solved a resource sharing problem using a novel interference-aware bipartite graph. A low complexity optimal packet scheduler was developed in [21] using an iterative process. Again, these works considered a single core network and most of them did not take interference into account.…”

Aggregation and Resource Scheduling in Machine-Type Communication Networks: A Stochastic Geometry Approach

“…Remark 2. Whenm grows above 2N, the last term in equality (a) of (17) becomes the largest contributor forK r , withK r → N(p r 1,2 + p r 2,2 ) = 2Np r succ since ∞ k=2N Pr(K = k) ≈ 1 and (16). Thus, when comparing with an L = 1 setup under the same circumstances, e.g., with average number of simultaneously served MTDs being Np OMA succ , the condition required for the hybrid access scheme 6 to overcome the OMA system (L = 1) is that p r succ > 1 2 p OMA succ .…”

“…By incorporating some intelligence in the aggregator, the network performance improves, as shown in [9], [13]- [16] for resource scheduling strategies. Among them, only [9] considers a more realistic scenario with a multi-cell network, hence the inter-cell interference, which is a critical issue, is taken into account.…”