Resource allocation for ultra-reliable low latency communications in sparse code multiple access networks

Abstract

In this paper, we propose an optimal resource allocation policy for sparse code multiple access (SCMA) networks supporting ultra-reliable low-latency communications (URLLC). The network is assumed to operate with finite blocklength (FBL) codes, which is opposed to the classical information-theoretic works with infinite blocklength (IBL) codes. In particular, we aim at maximizing the average transmission rate in the FBL regime while guaranteeing the transmission reliability. A joint design is proposed, which combines the power allocation with the codebook assignment. The convexity of the corresponding optimization problem is analyzed and an iterative search algorithm is further provided. We study the impact of reliability and short blocklength constraints on the performance of the proposed optimal resource allocation policy through numerical simulations. In addition, we evaluate the FBL performance of the proposed joint design in comparison to the scenario with an IBL assumption.

Refiner GAN Algorithmically Enabled Deep-RL for Guaranteed Traffic Packets in Real-Time URLLC B5G Communication Systems

“…However, this large set of actions makes RB and reliability unsuitable for deep-RL frameworks [20]. Previous studies [5], [8], [18], [19], [22], and [27] were unable to handle the large amounts of data involved in URLLC and demonstrated a high order time complexity, making them unsuitable for real-time requirements. However, deep-RL has difficulty achieving a large label of a real dataset in real-time.…”

“…We will use the derivation in Section II-D. An Action Space Reducer for RB and PA 𝑅 𝑖 𝑡 to the OFDMA resources 𝒫 𝑖𝑗 and 𝑧 𝑖𝑗 for all 𝑖 ∈ Ĭ, 𝑗 ∈ 𝒥 while decreasing the power in (Section II-D). Therefore, we will use the derivation in (Section II-E) for PGACL algorithm, whereas every UE achieves the data rate 𝑅 𝑖 𝑡 and attains a reward function as shown in (8) and transmits it as feedback to the deep-RL that uses this feedback and updates every UEs 𝑅 𝑖 𝑡 accordingly PGACL algorithm (Section II-E). The deep-RL framework is formally defined by its action-value function 𝒜, state-space 𝓢, and reward ℛ.…”

“…The number of bits to be transmitted in every TTI depends on the packet size of each UE. Deep-RL appropriated depends on whether the BS maximizes the reward function in (8), and the reliability for every UE must be guaranteed at the fixed point when 𝓋 𝑖 𝑡+1 = 𝓋 𝑖 𝑡 , also when the reliability is ѱ , the original optimization in (5b) is equivalent to (7d) to satisfy (7b). Therefore, if this constraint in (7b) is not satisfied in any time slot, the queue delay will depend on the number of arriving packets in the queue waiting for transmission, followed by the use of Lyapunov optimization to solve the RA problem related to time variation [22], [27].…”

“…RA for Orthogonal Frequency Division Multiple Access (OFDMA) can decrease the End-to-End (E2E) delay and achieve the stringent reliability of the uplink and Downlink (DL) transmission in mobile edge computing systems in [1], [6], [7]. This efficient RA algorithm is used to guarantee the maximum delay by offering a partial iteration between the uplink and the DL to reduce E2E latency [8]. B5G addresses several new service applications, such as drones, virtual reality, the advanced Internet of Things (IoT), Artificial Intelligence (AI), and autonomous driving.…”

Biological Resource Allocation Algorithms for Heterogeneous Uplink PD-SCMA NOMA Networks

“…Solutions to the considered NPhard optimization problem are derived through relaxation and application of convex methods. Work on RA in SCMA enabling ultra reliable low latency communications is considered in [15]. With the aim of maximizing transmit rate assuming finite block-length codes, the optimization problem is solved using Lagrangian based methods and an iterative algorithm implemented.…”