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Table Of Integrals Series And Products

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

Massive Access for 5G and Beyond

The increasing number of mobile users has given impetus to the demand for high data rate proximity services. The fifth-generation (5G) wireless systems promise to improve the existing technology according to the future demands and provide a road map for reliable and resource-efficient solutions. Device-to-device (D2D) communication has been envisioned as an allied technology of 5G wireless systems for providing services that include live data and video sharing. A D2D communication technique opens new horizons of device-centric communications, i.e., exploiting direct D2D links instead of relying solely on cellular links. Offloading traffic from traditional network-centric entities to D2D network enables low computational complexity at the base station besides increasing the network capacity. However, there are several challenges associated with D2D communication. In this paper, we present a survey of the existing methodologies related to aspects such as interference management, network discovery, proximity services, and network security in D2D networks. We conclude by introducing new dimensions with regard to D2D communication and delineate aspects that require further research.

5G D2D Networks: Techniques, Challenges, and Future Prospects

This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize joint active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity present in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

Massive machine-type communications (mMTC) is one of the main three focus areas in the 5th generation (5G) of wireless communications technologies to enable connectivity of a massive number of Internet of things (IoT) devices with little or no human intervention. In conventional human-type communications (HTC), due to the limited number of available channel resources and orthogonal resource allocation techniques, users get a transmission slot by making scheduling/connection requests. The involved control channel signaling, negligible with respect to the huge transmit data, is not a major issue. However, this may turn into a potential performance bottleneck in mMTC, where huge number of devices transmit short packet data in a sporadic way. To tackle the limited radio resources and massive connectivity challenges, non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows multiple users to simultaneously transmit their data over the same channel resource. This is achieved by employing user-specific signature sequences at the transmitting devices, which are exploited by the receiver for multi-user data detection. Due to its massive connectivity potential, NOMA has also been considered to enable grant-free transmissions especially in mMTC, where devices can transmit their data whenever they need without the scheduling requests. The existing surveys majorly discuss different NOMA schemes, and exploit their potential, in typical grant-based HTC scenarios, where users are connected with the base station, and various system parameters are pre-defined in the scheduling phase. Different from these works, this survey provides a comprehensive review of the recent advances in NOMA from a grant-free connectivity perspective. Various grant-free NOMA schemes are presented, their potential and related practical challenges are highlighted, and possible future directions are thoroughly discussed at the end.

Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey

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.

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

Device-to-device (D2D) communication can effectively meet the demanding high data rate by providing direct links among mobile users in cellular networks. In this paper, we analyze the transmission capacity of relay-assisted D2D communication coexisting with cellular networks in both overlay and underlay modes. D2D users can use the delicate spectrum resources in the overlay mode, while reuse the cellular resources in the underlay mode. Based on stochastic geometry, cellular users, D2D transmitters, and relay nodes (RNs) in the networks are all modeled as Poisson point process. Then, we calculate the RN existence probability and the expectation of relay link distance to obtain the successful transmission probabilities for D2D communication. According to two relay mechanisms for enhancing D2D transmission distance, we further obtain the transmission capacities of D2D communication with the assistance of RNs in both two modes, which reflect the influence from D2D density and power. In addition, the D2D transmission capacities with variable D2D link distance are also analyzed in two modes. Simulation results verify that D2D transmission capacity can be enhanced by relay transmission and influenced by a multitude of factors, including the user density, power, the D2D link distance, and the way of using RNs.

Transmission Capacity Analysis of Relay-Assisted Device-to-Device Overlay/Underlay Communication

The channel feedback overhead for massive multiple-input multiple-output systems with a large number of base station (BS) antennas is very high since the number of feedback bits of traditional codebooks scales linearly with the number of BS antennas. To reduce the feedback overhead, an effective codebook based on channel statistics has been designed, where the required number of feedback bits only scales linearly with the rank of the channel correlation matrix. However, this attractive conclusion was only proved under a particular channel assumption in the literature. To provide a rigorous theoretical proof under a general channel assumption, in this paper, we quantitatively analyze the performance of the channel-statistics-based codebook. Specifically, we first introduce the rate gap between the ideal case of perfect channel state information at the transmitter and the practical case of limited channel feedback, where we find that the rate gap depends on the quantization error of the codebook. Then, we derive an upper bound of the quantization error, based on which we prove that the required number of feedback bits to ensure a constant rate gap only scales linearly with the rank of the channel correlation matrix. Finally, numerical results are provided to verify this conclusion.

On the Performance of Channel-Statistics-Based Codebook for Massive MIMO Channel Feedback

In this paper, we consider space time spreading in the forward links of a MC-DS-CDMA system when random signature sequences are used. The performance of the system with different diversity order has also been analyzed. The numerical results show that considerable performance gain can be obtained by transmit diversity when the active user number is relatively small. As the number of the users increases, the diversity gain will decrease. It also suggests that it require multiuser interference cancellation when the active user number is relatively large.

Space time spreading in forward links of the multicarrier DS CDMA system

Device-to-device (D2D) communication can effectively meet the demands of high data rate by providing a direct link between two mobile users in cellular networks. In this paper, we analyze the optimal power control for energy efficiency of D2D communication underlaying cellular networks. Based on the stochastic geometry, we firstly use the homogeneous Poisson point process (PPP) to characterize the random distribution of both cellular and D2D users. Then, the successful transmission probabilities and energy efficiency (EE) of D2D communication are derived. We further analyze the optimization problem of D2D power with the constraints of outage and power. With the help of convex optimization, the convexity of D2D EE is verified, and the optimal D2D power for maximizing EE is evaluated in closed form. Simulation results demonstrate the impact of system parameters on the optimal D2D power as well as the influence of interference to the maximum EE of D2D communication.

Jianjun Li

Papers

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

Transmission Capacity Analysis of Relay-Assisted Device-to-Device Overlay/Underlay Communication

On the Performance of Channel-Statistics-Based Codebook for Massive MIMO Channel Feedback

Space time spreading in forward links of the multicarrier DS CDMA system

Optimal power control for energy efficiency of device-to-device communication underlaying cellular networks