Bichai Wang

Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends

The increasing demand of mobile Internet and the Internet of Things poses challenging requirements for 5G wireless communications, such as high spectral efficiency and massive connectivity. In this article, a promising technology, non-orthogonal multiple access (NOMA), is discussed, which can address some of these challenges for 5G. Different from conventional orthogonal multiple access technologies, NOMA can accommodate much more users via nonorthogonal resource allocation. We divide existing dominant NOMA schemes into two categories: power-domain multiplexing and code-domain multiplexing, and the corresponding schemes include power-domain NOMA, multiple access with low-density spreading, sparse code multiple access, multi-user shared access, pattern division multiple access, and so on. We discuss their principles, key features, and pros/cons, and then provide a comprehensive comparison of these solutions from the perspective of spectral efficiency, system performance, receiver complexity, and so on. In addition, challenges, opportunities, and future research trends for NOMA design are highlighted to provide some insight on the potential future work for researchers in this field. Finally, to leverage different multiple access schemes including both conventional OMA and new NOMA, we propose the concept of software defined multiple access (SoDeMA), which enables adaptive configuration of available multiple access schemes to support diverse services and applications in future 5G networks.

Comparison study of non-orthogonal multiple access schemes for 5G

With the development of mobile Internet and Internet of things (IoT), the 5th generation (5G) wireless communications will foresee explosive increase in mobile traffic. To address challenges in 5G such as higher spectral efficiency, massive connectivity, and lower latency, some non-orthogonal multiple access (NOMA) schemes have been recently actively investigated, including power-domain NOMA, multiple access with low-density spreading (LDS), sparse code multiple access (SCMA), multiuser shared access (MUSA), pattern division multiple access (PDMA), etc. Different from conventional orthogonal multiple access (OMA) schemes, NOMA can realize overloading by introducing some controllable interferences at the cost of slightly increased receiver complexity, which can achieve significant gains in spectral efficiency and accommodate much more users. In this paper, we will discuss basic principles and key features of three typical NOMA schemes, i.e., SCMA, MUSA, and PDMA. Whats more, their performance in terms of uplink bit error rate (BER) will be compared. Simulation results show that in typical Rayleigh fading channels, SCMA has the best performance, while the BER performance of MUSA and PDMA are very close to each other. In addition, we also analyze the performance of PDMA using the same factor graph as SCMA, which indicates that the performance gain of SCMA over PDMA comes from both the difference of factor graph and the codebook optimization.

Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

Non-orthogonal multiple access (NOMA) has been regarded as one of the promising key technologies for future 5G systems. In the uplink grant-free NOMA schemes, dynamic scheduling is not required, which can significantly reduce the signaling overhead and transmission latency. However, user activity has to be detected in grant-free NOMA systems, which is challenging in practice. In this letter, by exploiting the inherent structured sparsity of user activity naturally existing in NOMA systems, we propose a low-complexity multi-user detector based on structured compressive sensing to realize joint user activity and data detection. In particular, we propose a structured iterative support detection algorithm by exploiting such structured sparsity, which is able to jointly detect user activity and transmitted data in several continuous time slots. Simulation results show that the proposed scheme can achieve better performance than conventional solutions.

Dynamic Compressive Sensing-Based Multi-User Detection for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) can support more users than OMA techniques using the same wireless resources, which is expected to support massive connectivity for Internet of Things in 5G. Furthermore, in order to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this letter, by exploiting the temporal correlation of active user sets, we propose a dynamic compressive sensing (DCS)-based multi-user detection (MUD) to realize both user activity and data detection in several continuous time slots. In particular, as the temporal correlation of the active user sets between adjacent time slots exists, we can use the estimated active user set in the current time slot as the prior information to estimate the active user set in the next time slot. Simulation results show that the proposed DCS-based MUD can achieve much better performance than that of the conventional CS-based MUD in NOMA systems.

Spectrum and Energy-Efficient Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Array

The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems without obvious performance loss. However, the fundamental limit of existing beamspace MIMO is that the number of supported users cannot be larger than the number of RF chains at the same time-frequency resources. To break this fundamental limit, in this paper, we propose a new spectrum and energy-efficient mmWave transmission scheme that integrates the concept of non-orthogonal multiple access (NOMA) with beamspace MIMO, i.e., beamspace MIMO-NOMA. By using NOMA in beamspace MIMO systems, the number of supported users can be larger than the number of RF chains at the same time-frequency resources. In particular, the achievable sum rate of the proposed beamspace MIMO-NOMA in a typical mmWave channel model is analyzed, which shows an obvious performance gain compared with the existing beamspace MIMO. Then, a precoding scheme based on the principle of zero forcing is designed to reduce the inter-beam interferences in the beamspace MIMO-NOMA system. Furthermore, to maximize the achievable sum rate, a dynamic power allocation is proposed by solving the joint power optimization problem, which not only includes the intra-beam power optimization, but also considers the inter-beam power optimization. Finally, an iterative optimization algorithm with low complexity is developed to realize the dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.

Differential CSIT Acquisition Based on Compressive Sensing for FDD Massive MIMO Systems

To fully exploit advantages of massive MIMO, channel state information at the transmitter (CSIT) is essential to obtain the system performance gains. By far, both channel estimation and channel feedback have been proposed for FDD massive MIMO by exploiting the sparsity of CSI, but they are usually separately discussed, which may impair the CSIT acquisition performance and lead to unnecessary complex computation for users. In this paper, we propose the structured-CS based differential CSIT acquisition scheme for massive MIMO systems, where the downlink channel training and uplink channel feedback are jointly considered. Specifically, we first exploit the temporal correlation of time- varying channels to propose the differential CSIT acquisition scheme, which can reduce both the overhead for downlink training and uplink feedback. Then, we propose the structured compressive sampling matching pursuit (S-CoSaMP) algorithm to further reduce overhead by leveraging the structured sparsity of wireless MIMO channels. Moreover, the proposed differential operation and S-CoSaMP can also be used at users for better channel estimation performance if channel state information at the receiver is needed. Simulation results have demonstrated that the proposed scheme can achieve better CSIT acquisition performance than its counterparts.

Low-Complexity LSQR-Based Linear Precoding for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) using a large number of antennas at the base station (BS) is a promising technique for the next-generation 5G wireless communications. It has been shown that linear precoding schemes can achieve near-optimal performance in massive MIMO systems. However, classical linear precoding schemes such as zero- forcing (ZF) precoding suffer from high complexity due to the fact they require the matrix inversion of a large size. In this paper, we propose a low-complexity precoding scheme based on the least square QR (LSQR) method to realize the near-optimal performance of ZF precoding without matrix inversion. We show that the proposed LSQR-based precoding can reduce the complexity of ZF precoding by about one order of magnitude. Simulation results verify that the proposed LSQR-based precoding can provide a better tradeoff between complexity and performance than the recently proposed Neumann-based precoding.

Dynamic multi-user detection based on structured compressive sensing for IoT-oriented 5G systems

In Internet of things (IoT)-oriented 5G systems, more efficient multiple access is essential to handle the massive number of sporadic traffic generating IoT users/devices, which are inactive most of the time but regularly or irregularly access or leave the wireless network without human interaction. Non-orthogonal multiple access (NOMA) is a promising solution to support massive connectivity in future IoT-oriented 5G systems, where the dynamic multi-user detection (MUD) is required. In this paper, by exploiting the structured user activity sparsity, we propose the structured matching pursuit (SMP)-based dynamic MUD to jointly realize dynamic multi-user signal detection in several continuous time slots based on structured compressive sensing (SCS). Specifically, in several continuous time slots, the set of active users usually changes slowly due to continuous data transmission. Therefore, we can divide the active user sets into two different parts, i.e., the common active user set and the dynamic active user sets. Accordingly, the proposed SMP-based dynamic MUD simultaneously detects the common active user set in all time slots at first, and then the dynamic active user sets are detected in each single time slot individually. Simulation results show that the proposed SMP-based dynamic MUD can achieve much better performance than that of the conventional compressive sensing (CS)-based MUD, while they share very similar computational complexity.

Simultaneous Multi-Channel Reconstruction for TDS-OFDM Systems

Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has higher spectral efficiency than standard cyclic prefix OFDM (CP- OFDM), which is achieved by using a known pseudorandom noise (PN) sequence to replace the classical CP. However, due to the interference between the PN sequence and the data block, the performance of TDS-OFDM degrades severely over fast fading channels. To solve this problem, based on the distributed compressive sensing (DCS) theory, we propose an efficient way to realize simultaneous multi-channel reconstruction, which is achieved by using the inter-block-interference (IBI)-free region to reconstruct the high-dimensional sparse multipath channel. Specifically, we propose to utilize the temporal correlation of wireless channels as well as the channel property that path gains change much faster than path delays to simultaneously reconstruct multiple sparse channels. Then, we propose the parameterized channel estimation method based on simultaneous compressive sampling matching pursuit (S-CoSaMP) algorithm to achieve better channel estimation performance in fast time-varying channels. Simulation results demonstrate that the proposed scheme can achieve improved performance than conventional solutions.