An Analysis of Two-User Uplink Asynchronous Non-orthogonal Multiple Access Systems

Abstract

Recent studies have numerically demonstrated the possible advantages of the asynchronous nonorthogonal multiple access (ANOMA) over the conventional synchronous non-orthogonal multiple access (NOMA). The ANOMA makes use of the oversampling technique by intentionally introducing a timing mismatch between symbols of different users. Focusing on a two-user uplink system, for the first time, we analytically prove that the ANOMA with a sufficiently large frame length can always outperform the NOMA in terms of the sum throughput. To this end, we derive the expression for the sum throughput of the ANOMA as a function of signal-to-noise ratio (SNR), frame length, and normalized timing mismatch. Based on the derived expression, we find that users should transmit at full powers to maximize the sum throughput. In addition, we obtain the optimal timing mismatch as the frame length goes to infinity. Moreover, we comprehensively study the impact of timing error on the ANOMA throughput performance. Two types of timing error, i.e., the synchronization timing error and the coordination timing error, are considered. We derive the throughput loss incurred by both types of timing error and find that the synchronization timing error has a greater impact on the throughput performance compared to the coordination timing error. Index TermsNon-orthogonal multiple access, asynchronous transmission, oversampling, timing mismatch, interference cancellation.Results in this paper were presented in part at d 2N − r 2 d 2N −2 = r 1 (d 2N −2 − r 2 d 2N −4 ),

Downlink Asynchronous Non-Orthogonal Multiple Access Systems with Imperfect Channel Information

“…We assume that H 1 ∼ Exp(0.5) and H 2 ∼ Exp(1). We set the total transmit power of the BS P = 10 and τ = 0.5 since it has been proved in [2,3] that τ = 0.5 is the asymptotically optimal value to maximize the user throughput. For comparison, we employ the uniform quantizer proposed in [8] where the maximum quantization level L is derived by solving L = 1 λ∆ log 1 ∆ , λ is the parameter of the exponential distribution, and ∆ is the quantization bin width.…”

“…and (2) at the bottom of this page, H i = |h i | 2 is the channel gain of User i, Q = 2τ (1 − τ ), P is the total transmit power of BS, α ∈ (0, 1) is the power coefficient for the strong user (User 1 in this case), i.e., the powers allocated to Users 1 and 2 are αP and (1 − α)P , respectively. Note that NOMA can be considered as a special case of ANOMA, simply by setting τ = 0 in (2).…”

“…At the receiver, the composite signal is sampled at iT and (i+τ )T after matched filtering. This sampling method is called "oversampling" and the details have already been presented in [2,3]. For the sake of brevity, we omit it in this work.…”

“…If τ = 0, ANOMA degrades to NOMA and the received samples at iT and (i + τ )T are identical. The number of received samples is doubled in ANOMA (τ = 0) compared with NOMA, which then results in sampling diversity [2][3][4].…”

Improving NOMA Multi-Carrier Systems With Intentional Frequency Offsets

“…The results in [4] show that time asynchrony can increase the capacity region in a single carrier MAC channel. The benefits of time asynchrony in single carrier transmission for different scenarios like MIMO systems, relay networks and NOMA framework have been discussed in [5]- [10]. While time asynchrony in single carrier transmission can be beneficial, unfortunately, in multicarrier (MC) methods, the effect of time asynchrony turns into a phase shift and loses its benefits.…”