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Today's Massive MIMO cellular operation is dominated by orthogonal frequency division multiplexing (OFDM) modulation. One of the advantages of OFDM is the flexibility available to the base station to carve up the available spectrum into resource blocks (RBs) that can operate adjacent to one another. Massive MIMO adds spatial multiplexing layers on top of the RBs, enabling the simultaneous operation of dozens of UEs. In this paper, we present a resource allocation scheme for cyclic prefixed single carrier modulation (CP-SCM) waveforms using virtual antennas in a massive MIMO time domain duplexed scenario. Resources are quantized into data streams, and each user can be assigned a variable number of simultaneous streams. This paper provides additional analysis for the initial description of multi-stream processing (MSP) for uplink detection and leverages the uplink MSP framework for efficient downlink precoding. MSP is compared with current OFDM-based modulations. We also introduce heterogeneous MSP, where CP-SCM and OFDM signals can be processed in the same MSP framework.

Single carrier modulation (SCM) schemes are attractive for uplink (UL) transmissions due to improved power efficiency at the user equipment (UE) transmitter compared with multi-carrier modulation schemes. In a massive MIMO scenario with SCM, the UL detection must mitigate the effects of intersymbol interference and multi-user interference. This processing is effectively performed in the frequency domain (FD) using a minimum mean squared error (MMSE) detector when the transmission is framed with a cyclic prefix. This paper analyzes an MMSE-based detector compatible with distributed processing in a time-division duplex (TDD) system. The matrix inverses computed for the UL detection are then reused to perform multiuser precoding for the downlink (DL). We find that this scheme yields tremendous savings in computational complexity compared to commonly used zero-forcing (ZF) precoding without sacrificing any performance. Since MMSE processing introduces a bias to the estimates, we derive the scalar coefficients necessary to cancel the MMSE bias. The impact of channel estimation errors is analyzed for both the UL and DL cases in conjunction with a power-efficient approach to SCM channel estimation. Moreover, extensive simulations are performed to confirm our theoretical findings.