Random Access Protocols for Massive MIMO

5G wireless networks are expected to support new services with stringent requirements on data rates, latency and reliability. One novel feature is the ability to serve a dense crowd of devices, calling for radically new ways of accessing the network. This is the case in machine-type communications, but also in urban environments and hotspots. In those use cases, the high number of devices and the relatively short channel coherence interval do not allow per-device allocation of orthogonal pilot sequences. This article motivates the need for random access by the devices to pilot sequences used for channel estimation, and shows that Massive MIMO is a main enabler to achieve fast access with high data rates, and delay-tolerant access with different data rate levels. Three pilot access protocols along with data transmission protocols are described, fulfilling different requirements of 5G services

Efficient C-RAN Random Access for IoT Devices: Learning Links via Recommendation Systems

We focus on C-RAN random access protocols for IoT devices that yield low-latency high-rate active-device detection in dense networks of large-array remote radio heads. In this context, we study the problem of learning the strengths of links between detected devices and network sites. In particular, we develop recommendation-system inspired algorithms, which exploit random-access observations collected across the network to classify links between active devices and network sites across the network. Our simulations and analysis reveal the potential merit of data-driven schemes for such on-the-fly link classification and subsequent resource allocation across a wide-area network.Comment: This manuscript has been submitted to 2018 IEEE International Conference on Communications Workshops (ICC Workshops): Promises and Challenges of Machine Learning in Communication Network

Pilot Allocation and Sum-Rate Analysis in Cell-Free Massive MIMO Systems

This paper deals with the challenging issue of the unaffordable channel training overhead in the dense cell-free massive multi-input multi-output (MIMO) system when a high number of users are being simultaneously served. By adopting the user-centric cluster method, a dynamic pilot reuse (DPR) scheme is proposed to allow a pair of users to share a single pilot sequence. Specifically, the proposed reuse scheme is achieved with the objective of maximizing the uplink achievable sum-rate subject to users' signal to interference plus noise ratio (SINR) requirements and pilot resources constraints. On this basis, the SINR expression is derived for any user sharing its pilot with another by utilizing both minimum mean squared error (MMSE) detection and channel estimation. A low complexity pilot reuse algorithm is then developed based on the separation distance between users. The iterative grid search (IGS) method is employed to find the threshold that can be utilized in the proposed algorithm to maximize the sum-rate. Finally, simulation results are presented to show the effectiveness of the DPR scheme with the optimized threshold in terms of the uplink achievable sum-rate

Pilot Allocation and Sum-rate Analysis in Distributed Massive MIMO Systems

In distributed massive multi-input multi-output (DM-MIMO) systems, orthogonal pilot sequences are generally utilized to acquire the channel state information (CSI). However, this highly restricts the number of users simultaneously served. In this paper, a pilot reuse within a single cell DM-MIMO system is proposed to serve more users than the available pilot sequences. The reuse in this strategy is applied so that maximum achievable sum-rate is satisfied with the constraint of predefined pilot resource. On this basis, two users in different subcells separated by a large distance and satisfying a specific signal to interference plus noise ratio (SINR) level can share the same pilot sequence. An expression for SINR is derived for any pair of users who use the same pilot. Based on this expression, an algorithm is proposed to choose which pairs of users are able to use the same pilot with the constraint of satisfying the minimum SINR required for these users. The simulation results demonstrate that the uplink achievable sum-rate for the proposed strategy is higher than both cases when no pilot reuse or random pilot reuse are considered

Interference Exploitation via Symbol-Level Precoding: Overview, State-of-the-Art and Future Directions

Interference is traditionally viewed as a performance limiting factor in wireless communication systems, which is to be minimized or mitigated. Nevertheless, a recent line of work has shown that by manipulating the interfering signals such that they add up constructively at the receiver side, known interference can be made beneficial and further improve the system performance in a variety of wireless scenarios, achieved by symbol-level precoding (SLP). This paper aims to provide a tutorial on interference exploitation techniques from the perspective of precoding design in a multi-antenna wireless communication system, by beginning with the classification of constructive interference (CI) and destructive interference (DI). The definition for CI is presented and the corresponding mathematical characterization is formulated for popular modulation types, based on which optimization-based precoding techniques are discussed. In addition, the extension of CI precoding to other application scenarios as well as for hardware efficiency is also described. Proof-of-concept testbeds are demonstrated for the potential practical implementation of CI precoding, and finally a list of open problems and practical challenges are presented to inspire and motivate further research directions in this area