NOMA in the Uplink: Delay Analysis With Imperfect CSI and Finite-Length Coding

Abstract

We study whether using non-orthogonal multiple access (NOMA) in the uplink of a mobile network can improve the performance over orthogonal multiple access (OMA) when the system requires ultrareliable low-latency communications (URLLC). To answer this question, we first consider an ideal system model with perfect channel state information (CSI) at the transmitter and long codewords, where we determine the optimal decoding orders when the decoder uses successive interference cancellation (SIC) and derive closed-form expressions for the optimal rate when joint decoding is used. While joint decoding performs well even under tight delay constraints, NOMA with SIC decoding often performs worse than OMA. For low-latency systems, we must also consider the impact of finite-length channel coding, as well as rate adaptation based imperfect CSI. We derive closed-form approximations for the corresponding outage or error probabilities and find that those effects create a larger performance penalty for NOMA than for OMA. Thus, NOMA with SIC decoding may often be unsuitable for URLLC. Index TermsNonorthogonal multiple access (NOMA), stochastic network calculus, effective capacity, quality of service, delay performance, URLLC, imperfect CSI, finite blocklength regime (SIC), i.e., decode one of the signals, and then subtract the corresponding codeword from the received signal, such that the other signal is interference-free. As a result, NOMA can increase the sum ergodic capacity of the system [1].However, the ergodic capacity is not a meaningful performance metric for applications that require ultra-reliable low-latency communications (URLLC). For example, industrial control systems often require latencies of at most a few milliseconds. The probability of violating this deadline must be very small, with target values of 10 −6 and below [2]. In contrast to the ergodic sum capacity, the delay violation probability is affected by the SIC decoding order: the user that is decoded first faces interference by the second user and thus experiences a lower data rate than if it were decoded last. Although the sum rate remains the same regardless of the decoding order, a very low rate for one of the users can mean that the user's data cannot be transmitted and must be buffered, leading to a queueing delay. The delay performance of the two-user NOMA uplink thus depends on the optimal trade-off between the two decoding orders with respect to the users' delay constraints. Furthermore, both SIC decoding orders may lead to a low rate for one of the users. We need to consider a more general joint decoding scheme that can also achieve intermediate rate points. Even though joint decoding does not increase the ergodic sum capacity, it may improve the delay performance by avoiding very low rates.The above discussion on the rate adaptation assumed that the base station has perfect knowledge of the SNR of both users, and that the users can communicate without errors at a rate equal to the capacity of the channel. However, these assumptions become highly...

Position Information-Based NOMA for Downlink and Uplink Transmission in Mobile Scenarios

“…By substituting (16) into (15), this corollary is proved. Given P 1 e < 0.5 and D > 1, one can infer from (14) that the error probability is larger than that of the fading-free scenario.…”

“…Actually, this assumption might not be realistic due to many limitations like high mobility of users and channel estimation error. Therefore, some works focused on the case of partial channel state information(CSI) for downlink [11]- [14] and uplink transmission [15], [16]. The impact of channel estimation error on NOMA system was investigated in [11], [12].…”

“…Moreover, the impact of imperfect CSI on uplink NOMA detection was analyzed in [15]. The authors of [16] evaluated NOMA in a low-latency system and showed that imperfect CSI creates a larger penalty for NOMA than for OMA. For the past several years, some works focused on NOMA with distance information [17].…”

Downlink Analysis of NOMA-Enabled Cellular Networks With 3GPP-Inspired User Ranking

“…For such applications, it is important to consider RA formulations that explicit consider delay constraints. In this context, the effective capacity, defined in [32] as the maximum achievable arrival rate that satisfy a delay QoS constraint, is analyzed for NOMA in [33]- [35] for the downlink case and in [36], [37] for the uplink case. However, in general, the existing works on the delay analysis of NOMA are relatively sparse.…”

“…However, in general, the existing works on the delay analysis of NOMA are relatively sparse. Besides, the aforementioned works on RA with the focuse on sum rate [8], [9], [25], [28]- [31] and effective capacity [33]- [37] are limited to the single cell and hence do not capture the impact of inter-cell interference.…”