Edge computing and power control in NOMA‐enabled cognitive radio networks

Abstract

Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

“…The summary of the existing works on NOMA MEC is provided in Table 3. The work in [82] considered minimizing the total completion time of secondary users in a cognitive NOMA-MEC system. The latency minimization problem is optimized under constraints that the interference at primary users is below an interference threshold and the total computing resources assigned to users cannot exceed the maximum computing capability of the MEC server.…”

“…A joint offloading decision, local computing capability control, and NOMA power allocation was considered to minimize the system delay. Similar to [82], the decomposition technique is applied to solve the problem in [83] in an iterative manner.…”

A taxonomy of blockchain envisioned edge‐as‐a‐connected autonomous vehicles

“…• Network bandwidth: MEC minimizes the data transfer cost by keeping critical data at the edge of the device or device itself. 27 This ultimately reduces the network bandwidth and CAVs need to manage it properly to get faster access to data.…”

Quality ensured non‐orthogonal multiple access based downlink scalable video coding multicasting over cognitive radio network

“…In References 39 and 40 cellular architecture has been considered for SVC over NOMA, and resource allocation for NOMA‐based CRN with SWIPT is analyzed in Reference 41. In Reference 42, authors have proposed mobile edge computing to minimize the total computation time of SUs, and a joint offloading decision along with a power control algorithm is recommended.…”