Asynchronous Channel Training in Multi-Cell Massive MIMO

Pilot contamination has been regarded as the main bottleneck in time division duplexing (TDD) multi-cell massive multiple-input multiple-output (MIMO) systems. The pilot contamination problem cannot be addressed with large-scale antenna arrays. We provide a novel asynchronous channel training scheme to obtain precise channel matrices without the cooperation of base stations. The scheme takes advantage of sampling diversity by inducing intentional timing mismatch. Then, the linear minimum mean square error (LMMSE) estimator and the zero-forcing (ZF) estimator are designed. Moreover, we derive the minimum square error (MSE) upper bound of the ZF estimator. In addition, we propose the equally-divided delay scheme which under certain conditions is the optimal solution to minimize the MSE of the ZF estimator employing the identity matrix as pilot matrix. We calculate the uplink achievable rate using maximum ratio combining (MRC) to compare asynchronous and synchronous channel training schemes. Finally, simulation results demonstrate that the asynchronous channel estimation scheme can greatly reduce the harmful effect of pilot contamination

On the Performance of MRC Receiver with Unknown Timing Mismatch-A Large Scale Analysis

There has been extensive research on large scale multi-user multiple-input multiple-output (MU-MIMO) systems recently. Researchers have shown that there are great opportunities in this area, however, there are many obstacles in the way to achieve full potential of using large number of receive antennas. One of the main issues, which will be investigated thoroughly in this paper, is timing asynchrony among signals of different users. Most of the works in the literature, assume that received signals are perfectly aligned which is not practical. We show that, neglecting the asynchrony can significantly degrade the performance of existing designs, particularly maximum ratio combining (MRC). We quantify the uplink achievable rates obtained by MRC receiver with perfect channel state information (CSI) and imperfect CSI while the system is impaired by unknown time delays among received signals. We then use these results to design new algorithms in order to alleviate the effects of timing mismatch. We also analyze the performance of introduced receiver design, which is called MRC-ZF, with perfect and imperfect CSI. For performing MRC-ZF, the only required information is the distribution of timing mismatch which circumvents the necessity of time delay acquisition or synchronization. To verify our analytical results, we present extensive simulation results which thoroughly investigate the performance of the traditional MRC receiver and the introduced MRC-ZF receiver

An Analysis of Two-User Uplink Asynchronous Non-Orthogonal Multiple Access Systems

Recent studies have numerically demonstrated the possible advantages of the asynchronous non-orthogonal multiple access (ANOMA) over the conventional synchronous non-orthogonal multiple access (NOMA). The ANOMA makes use of the oversampling technique by intentionally introducing a timing mismatch between symbols of different users. Focusing on a two-user uplink system, for the first time, we analytically prove that the ANOMA with a sufficiently large frame length can always outperform the NOMA in terms of the sum throughput. To this end, we derive the expression for the sum throughput of the ANOMA as a function of signal-to-noise ratio (SNR), frame length, and normalized timing mismatch. Based on the derived expression, we find that users should transmit at full powers to maximize the sum throughput. In addition, we obtain the optimal timing mismatch as the frame length goes to infinity. Moreover, we comprehensively study the impact of timing error on the ANOMA throughput performance. Two types of timing error, i.e., the synchronization timing error and the coordination timing error, are considered. We derive the throughput loss incurred by both types of timing error and find that the synchronization timing error has a greater impact on the throughput performance compared to the coordination timing error

Novel Time Asynchronous NOMA schemes for Downlink Transmissions

In this work, we investigate the effect of time asynchrony in non-orthogonal multiple access (NOMA) schemes for downlink transmissions. First, we analyze the benefit of adding intentional timing offsets to the conventional power domain-NOMA (P-NOMA). This method which is called Asynchronous-Power Domain-NOMA (AP-NOMA) introduces artificial symbol-offsets between packets destined for different users. It reduces the mutual interference which results in enlarging the achievable rate-region of the conventional P-NOMA. Then, we propose a precoding scheme which fully exploits the degrees of freedom provided by the time asynchrony. We call this multiple access scheme T-NOMA which provides higher degrees of freedom for users compared to the conventional P-NOMA or even the modified AP-NOMA. T-NOMA adopts a precoding at the base station and a linear preprocessing scheme at the receiving user which decomposes the broadcast channel into parallel channels circumventing the need for Successive Interference Cancellation (SIC). The numerical results show that T-NOMA outperforms AP-NOMA and both outperform the conventional P-NOMA. We also compare the maximum sum-rate and fairness provided by these methods. Moreover, the impact of pulse shape and symbol offset on the performance of AP-NOMA and T-NOMA schemes are investigated

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed