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Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA

Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor

TL;DR

A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users’ expected capacity, to enable a capacity-optimal network.

Abstract

Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks, where IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol. To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity. It is shown that the network optimization objective is high dimensional and mathematically intractable, yet it admits favourable mathematical properties that enable the design of efficient data-driven algorithmic solutions which do not require a priori knowledge of the channel model or network topology. A centralized model-based algorithm and a scalable distributed model-free algorithm, are proposed to optimally tune the transmission probabilities of IoT devices and attain the maximum capacity. The convergence of the proposed algorithms to the optimal solution is further established based on convex optimization and game-theoretic analysis. Extensive simulations demonstrate the merits of the novel formulation and the efficacy of the proposed algorithms.

Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA

TL;DR

A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users’ expected capacity, to enable a capacity-optimal network.

Abstract

Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks, where IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol. To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity. It is shown that the network optimization objective is high dimensional and mathematically intractable, yet it admits favourable mathematical properties that enable the design of efficient data-driven algorithmic solutions which do not require a priori knowledge of the channel model or network topology. A centralized model-based algorithm and a scalable distributed model-free algorithm, are proposed to optimally tune the transmission probabilities of IoT devices and attain the maximum capacity. The convergence of the proposed algorithms to the optimal solution is further established based on convex optimization and game-theoretic analysis. Extensive simulations demonstrate the merits of the novel formulation and the efficacy of the proposed algorithms.

Paper Structure

This paper contains 15 sections, 7 theorems, 36 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model
  4. Problem Formulation
  5. Centralized Learning-Based Optimization Algorithm
  6. System Identification
  7. System Control
  8. The centralized learning algorithm
  9. Distributed Learning-Based Optimization Algorithm
  10. Best Response Dynamics with Action Aggregation
  11. Performance Evaluation
  12. Comparison of Learning Algorithms
  13. Network Densification with NOMA
  14. Heterogeneous Access
  15. Conclusion

Key Result

Theorem 1

In a multi-cell $p$-persistent slotted Aloha system of $N=2$ users and $M\in \{1,2\}$ BSs with NOMA, where users attempt to transmit to the closest BS with probability $p_i, \forall i \in \{1,2\}$ in each slot, the expected rate of a user $i$, $\bar{R}_i(p_1,p_2), \forall i \in \{1,2\}$, is a strict

Figures (5)

  • Figure 1: Input Convex (Concave) Feed-Forward Neural Network
  • Figure 2: Mean approximation ratio comparison of proposed and baseline optimization algorithms on $10$ random deployments of (a) $N=4$, (b) $N=8$, and (c) $N=16$ IoT devices and $M=2$ BSs.
  • Figure 3: Optimization trajectories of different optimization methods on a random topology of $N=16$ IoT devices and $M=2$ BSs.
  • Figure 4: Geometric mean of expected user rates with and without NOMA in random deployments of (a) $M=2$ BSs and variable number of IoT devices $N$, and (b) $N=200$ IoT devices and variable number of BSs.
  • Figure 5: Heatmaps of the optimal heterogeneous access transmission probabilities learned by the distributed algorithm for a mesh grid deployment of $N=144$ IoT devices (blue squares) and (a) $M=1$ BS without NOMA, (b) $M=1$ BS with NOMA, (d) $M=4$ BSs without NOMA, (e) $M=4$ BSs with NOMA. In (a,b,d,e), red circles represent BSs, while the shade of blue squares correspond to the transmission probability of IoT devices (darker shades correspond to higher transmission probability). In (c), a boxplot of the achievable expected user rates for the four networks considered in (a,b,d,e) is shown. In (f), Jain fairness gain of heterogeneous access is shown with respect to optimal homogeneous access that maximizes the arithmetic mean of expected user rates.

Theorems & Definitions (17)

  • Definition 1
  • Theorem 1
  • Proof
  • Theorem 2
  • Proof
  • Definition 2
  • Proposition 1
  • Proof
  • Definition 3
  • Lemma 1
  • ...and 7 more