Cooperative Non-Orthogonal Layered Multicast Multiple Access for Heterogeneous Networks

Abstract

This paper proposes a novel design of cooperative non-orthogonal layered multicast multiple access in a heterogeneous network, where the information is encoded into the messages of high-priority (HP) and low-priority (LP). Two types of multicast users coexist in the network: 1) regular users (RUs), which are located far away from the base-station (BS) and expect to decode only the HP message (due to the weak channels); 2) advanced users (AUs), which are located close to the BS and expect to decode both HP and LP messages. To improve the reliability of layered multicast, we consider that the successful AUs (those AUs who successfully decode the HP and LP messages) serve as potential relays to assist other AUs/RUs. Based on this idea, two novel cooperation strategies are proposed for different cases of channel information availability. For each proposed strategy, we derive closed-form exact outage probabilities of AUs and RUs, and then further analyze their diversity orders. Moreover, considering that the layered multicast is outage-constrained, we theoretically evaluate the energy consumption of both strategies and demonstrate their energy saving gains over the direct nonorthogonal multiple access for layered multicast. Finally, our theoretical analysis is verified by numerical results, and the advantages of the proposed strategies are also demonstrated.

Efficient Resource Allocation for Mobile-Edge Computing Networks With NOMA: Completion Time and Energy Minimization

“…Proof: Refer to Appendix F. , and calculate D ij andT i according to (18) and (21), respectively. 3: Check the feasibility conditions (23) and (24). If (20) has a feasible solution, set T max = T .…”

“…From Appendix D, the complexity of solving Problem (17) For the spacial case with ω = 1, the optimal solution of Problem (10) is obtained by using Algorithm 2. According to Algorithm 2, the main complexity in each step lies in checking the feasibility conditions (23) and (24), which involves complexity O(M). As a result, the total complexity of Algorithm 2 is O(M log 2(1/ǫ)), where O(log 2(1/ǫ)) is the complexity of the bisection method with accuracy ǫ.…”

Opportunistic Adaptive Non-Orthogonal Multiple Access in Multiuser Wireless Systems: Probabilistic User Scheduling and Performance Analysis

“…To enhance the spectral efficiency and reduce the co-channel interference, user scheduling has been integrated into cooperative NOMA in some recent work [20]- [26]. Specifically, only the users that are scheduled can participate in the cooperative NOMA transmissions, which is formally called opportunistic cooperative NOMA.…”

“…Note that these studies [20]- [23] only focus on wireless unicast applications, where all users require individually different information. When some users demand the same data via wireless multicast, several user scheduling schemes are proposed in [24]- [26]. In these schemes, one of the users that have successfully decoded the multicast messages is selected to forward all its decoded messages.…”

Outage Probability Analysis for the Multi-Carrier NOMA Downlink Relying on Statistical CSI

“…Therefore, the outage probability of failing to support a minimum target rate is a critical performance metric. The outage analysis of NOMA has received considerable research attention both under the assumptions of only having statistical CSI 1 (e.g., [8]- [12]), imperfect CSI (e.g., [13], [14]), and also imperfect SIC (e.g., [15], [16]). These studies have been focused mainly on single-carrier NOMA systems.…”