Massive Connectivity with Massive MIMO-Part I: Device Activity Detection and Channel Estimation (1706.06438v2)

Abstract: This two-part paper considers an uplink massive device communication scenario in which a large number of devices are connected to a base-station (BS), but user traffic is sporadic so that in any given coherence interval, only a subset of users are active. The objective is to quantify the cost of active user detection and channel estimation and to characterize the overall achievable rate of a grant-free two-phase access scheme in which device activity detection and channel estimation are performed jointly using pilot sequences in the first phase and data is transmitted in the second phase. In order to accommodate a large number of simultaneously transmitting devices, this paper studies an asymptotic regime where the BS is equipped with a massive number of antennas. The main contributions of Part I of this paper are as follows. First, we note that as a consequence of having a large pool of potentially active devices but limited coherence time, the pilot sequences cannot all be orthogonal. However, despite the non-orthogonality, this paper shows that in the asymptotic massive multiple-input multiple-output (MIMO) regime, both the missed device detection and the false alarm probabilities for activity detection can always be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in the user activity pattern. Part II of this paper further characterizes the achievable rates using the proposed scheme and quantifies the cost of using non-orthogonal pilot sequences for channel estimation in achievable rates.

• The paper demonstrates that using compressed sensing and AMP enables near-perfect device activity detection in massive MIMO systems.
• It systematically analyzes the use of non-orthogonal pilot sequences, showing that flawless detection is achievable in the asymptotic regime.
• The research highlights that residual channel estimation errors limit data rates, prompting further exploration for improved protocols.

Massive Connectivity with Massive MIMO--Part I: Device Activity Detection and Channel Estimation

This paper investigates a pivotal aspect of massive Internet-of-Things (IoT) and machine-type communications (MTC), focusing on uplink communication scenarios in massive Multiple Input Multiple Output (MIMO) systems. A significant challenge in such systems arises from the need to manage massive connectivity, where a substantial number of sporadic user devices are connected to a base station (BS). The critical issue addressed is the efficient detection of active devices and the estimation of their channels, crucial for optimizing the overall achievable rate in a communication system characterized by sporadic traffic patterns.

Key Contributions

The paper meticulously analyzes the cost of device activity detection and channel estimation within a grant-free two-phase access protocol. Here, the first phase is reserved for joint device activity detection and channel estimation using pilot sequences, while the second phase is allocated for data transmission. The analysis is conducted under an asymptotic massive MIMO regime, whereby the BS is equipped with a prolific number of antennas, enhancing the paper's relevance to practical large-scale IoT systems.

Main Results:

• Non-Orthogonal Pilot Sequences: The challenge of limited coherence time precludes the use of orthogonal pilot sequences for all devices. The authors propose using compressed sensing techniques to exploit sparsity in user activity, demonstrating that in the massive MIMO regime, both missed detections and false alarms can be asymptotically reduced to zero.
• State Evolution Analysis: Utilizing the Approximate Message Passing (AMP) algorithm, the paper provides a precise state evolution analysis, establishing that the expected residual noise in the channel estimation is Gaussian and uncorrelated across receiving antennas even under non-linear user activity detection.
• Channel Estimation: The research characterizes the deterioration in channel estimation accuracy due to the use of non-orthogonal pilot sequences and highlights that while device activity detection becomes flawless in the limit of infinite antennas, channel estimation error remains a limiting factor for achievable rates.

Technical Insights and Theoretical Implications

The comprehensive asymptotic analysis reinforces that massive MIMO systems are well-suited for addressing the demands of massive connectivity. Theoretically, it affirms the feasibility of attaining perfect device detection in systems equipped with a large number of antennas, a notable deduction for designing future wireless networks supporting expansive IoT deployments.

Theoretical Implications:

• Large-Scale Systems: The results point to a regime where despite the enormous number of potential users, effective resource management is achievable with finite and reasonably sized pilot sequences, if the BS has sufficient antennas.
• Channel Estimation Boundaries: While detection advantages are significant, the remaining channel estimation error suggests a boundary in achievable performance, thus recommending further exploration into mitigating these errors in practical scenarios.

Future Prospects and Practical Implications

Future expansions of this research could explore adaptive protocols for pilot sequence assignment, potentially integrating machine learning techniques to further improve channel estimation under vendor-specific constraints. Moreover, an investigation into robustness under real-world conditions, such as channel fading and interference, would be crucial in advancing the applicability of these theoretical findings.

In summary, this research provides valuable insights into harnessing massive MIMO technology for efficiently managing ultra-dense networks of devices, critical for the advancement of IoT and MTC within 5G and beyond. Part II of this paper will further elucidate the achievable rate characterization, thereby offering a complete perspective on system design optimization in massive connectivity scenarios.