Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth generation wireless communication networks, which is expected to support massive connectivity for massive machine-type communications (mMTC). Due to the sporadic communication nature of mMTC, the grant-free transmission methodology is highly expected in uplink NOMA systems, to drastically reduce the transmission latency and signaling overhead. Exploiting the inherent sparsity nature of user activity, compressive sensing (CS) techniques have been applied for efficient multi-user detection in the uplink grant-free NOMA. In this paper, we propose a prior-information-aided adaptive subspace pursuit (PIA-ASP) algorithm to improve the multi-user detection performance. In this algorithm, a parameter evaluating the quality of the prior-information support set is introduced, in order to exploit the intrinsically temporal correlation of active user support sets in several continuous time slots adaptively. Then, to mitigate the incorrect estimation effect of the prior support quality information, a robust PIA-ASP algorithm is further proposed, which adaptively exploits the prior support based on the corresponding support quality information in a conservative way. It is noted that both of the two proposed algorithms do not require the knowledge of the user sparsity level, while most of the state-of-the-art CS-based multi-user detection algorithms usually need. Moreover, for the two proposed algorithms, the upper bound of the signal detection error and the computational complexity is derived. Simulation results demonstrate that the two proposed algorithms are capable of achieving much better performance than that of the existing CS-based multi-user detection algorithms with a similar computational complexity.

Efficient Multi-User Detection for Uplink Grant-Free NOMA: Prior-Information Aided Adaptive Compressive Sensing Perspective

Wireless data traffic is growing extraordinarily, with new wireless devices such as smartphones and bandwidth-demanding wireless applications such as video streaming becoming increasingly popular and widely adopted. Correspondingly, we have also witnessed the phenomenal wireless technology evolutions to support higher system capacities from generation to generation. Long Term Evolution has been developed as a 4G wireless technology that can support next generation multimedia applications with high capacity and high mobility needs. However, the peak data rate from 3G UMTS to 4G LTE-Advanced only increases 55 percent annually, while global mobile traffic has increased 66 times with an annual growth rate of 131 percent between 2008 and 2013. Clearly, there is a huge gap between the growth rate of the new air interface and the growth rate of customers? needs. A promising way to alleviate the contention between the actual traffic demands and the actual system capacity growth is to exploit more available spectrum resources. Recently, cognitive radio technology has been under extensive research and study. It aims to provide abundant new spectrum opportunities by exploiting underutilized or unutilized spectrum opportunistically. In this article, we discuss the technical solutions to expand LTE spectrum with CR technology (LTE-CR), and survey the advances in LTE-CR from both research and implementation aspects. We present detailed key technologies that enable LTE-CR in the TV white space (TVWS), and related standards and regulation progresses. To demonstrate the feasibility of deploying LTECR in TVWS, we have conducted extensive system-level simulations and also developed a LTE-CR prototype. Both simulation and laboratory testing results show that applying LTECR in TVWS can achieve satisfactory performance.

Expanding LTE network spectrum with cognitive radios: From concept to implementation

This paper concerns uplink grant-free nonorthogonal multiple access, where the handshaking procedure is not required to reduce control signaling overhead and transmission latency. In especially the dynamic scenarios, e.g., Internet of vehicles, the active users have to be identified and their channel state information needs to be estimated before performing multiuser detection. We investigate the joint user activity detection (UAD) and channel estimation (CE), which provides necessary information for data detection. In this paper, the joint UAD and CE is formulated as a block sparse signal recovery problem. First, the block orthogonal matching pursuit (BOMP) algorithm is studied for this problem, but its complexity grows with the fourth power of active user number, which hinders its application. Then, block sparse Bayesian learning (BSBL) is investigated to solve this problem, and in particular a low complexity message passing based implementation of BSBL with belief propagation and mean field is developed. The proposed message passing based BSBL (MP-BSBL) algorithm has a complexity independent of active user number, which can be significantly lower than that of the BOMP algorithm. In addition, MP-BSBL provides an estimate of the noise power, which can be readily used for data detection. Simulation results show that the MP-BSBL algorithm delivers almost the same performance as BOMP with the exact knowledge of active user number and can reach the performance bound for channel estimation.

Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation for Grant-Free NOMA Systems

This paper considers random multiple access in a network where only a small portion of users have data to forward and transmit packets in each time slot because the user activity ratio is not high in practice. For this reason, the access point (AP) has to not only identify the users who transmitted but also decode the received data codewords. Exploiting the sparsity of transmitting users, Lasso, which is a well-known practical compressed sensing algorithm, is applied for efficient user identification. The compressed sensing algorithm enables the AP to handle more users than the conventional random multiple access schemes do. We develop distributed scheduling methods for maximizing the system sum throughput, and we analyze the corresponding optimal throughput for three different cases of channel knowledge, i.e., the channel state information at the transmitter (CSIT), the channel state information at the receiver (CSIR), and the imperfect channel state information at the receiver (ImCSIR). We also derive the closed-form expressions of asymptotically optimal scheduling parameters and the corresponding maximum sum throughput for each CSI assumption. The results show the effects of system parameters on the sum throughput and provide useful insights on using compressed sensing for throughput maximization in random multiple access schemes.

Sparsity Controlled Random Multiple Access With Compressed Sensing

Non-orthogonal multiple access (NOMA) has become one of the promising key technologies for future 5G wireless communications to improve spectral efficiency and support massive connectivity. However, in the uplink grant-free NOMA system, the current near-optimal multi-user detection (MUD) based on message passing algorithm (MPA) assumes that the user activity information is exactly known at the receiver, which is impractical yet challenging due to anyone of massive users can randomly enter or leave the system. In this paper, inspired by the observation of user sparsity, we jointly use compressive sensing (CS) and MPA to propose a CS-MPA detector to realize both user activity and data detection for uplink grant-free NOMA. Specifically, the MUD problem is firstly formulated under CS framework by exploiting user sparsity, and then user activity can be detected by sparse signal recovery algorithms in CS. Then, MPA can be performed to reliably detect active users' data. It is shown that the proposed CS-MPA detector with affordable complexity not only outperforms the conventional MPA detector without user activity information, but also achieves very close performance to the genie- knowledge MPA detector with exact knowledge of user activity, especially when the signal-to-noise ratio (SNR) is high.

Compressive Sensing Based Multi-User Detection for Uplink Grant-Free Non-Orthogonal Multiple Access

Detailed knowledge about the traffic mixture is essential for network operators and administrators, as it is a key input for numerous network management activities. Several traffic classification approaches co-exist in the literature, but none of them performs well for all different application traffic types present in the Internet. In this study we compare and benchmark the currently known traffic classification methods on network traces captured in an operational 3G mobile network. Utilizing the experiences about the strengths and weaknesses of the existing approaches, a novel combined method is proposed aiming at improving the completeness and accuracy of classification. The novel method is based on a complex decision mechanism, which can provide appropriate identification for each different application type. As a main contribution, with the help of the new method it is shown that applications previously used only in fixed access networks may appear in mobile broadband environment.

Traffic analysis of mobile broadband networks

A method is provided of encoding a signature string X that is to be searched for within a search string. Each character in the search string is one of n characters of an alphabet and each character in the signature string X is one of the n characters or a wildcard character. The method comprising: encoding the signature string X into a first part B and a second part R with reference to a dictionary comprising a plurality of codes. The first part B identifies which, if any, characters of the signature string X are wildcard characters. The second part R is formed by, for each character in the signature string X that is not a wildcard character, retrieving a code from the dictionary based on the character and its position within the signature string X, the dictionary holding a different code for each such character-position pairing, and combining the retrieved codes according to a predetermined logical operation (e.g. XOR) to form the second part R. A method is also provided for searching for a signature string within a search string. The methods proposed are particularly suitable for use on Single Instruction Multiple Data (SIMD) architectures, such as Graphical Processing Units, and use of the methods is proposed for classifying traffic in a communications or computer network.

Sandor Gyori

Papers

Traffic analysis of mobile broadband networks

String matching method and apparatus

Traffic Analysis of Mobile Broadband Networks