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The use of base stations (BSs) and access points (APs) with a large number of antennas, called Massive MIMO (multiple-input multiple-output), is a key technology for increasing the capacity of 5G networks and beyond. While originally conceived for conventional sub-6 GHz frequencies, Massive MIMO (mMIMO) is ideal also for frequency bands in the range 30-300 GHz, known as millimeter wave (mmWave). Despite conceptual similarities, the way in which mMIMO can be exploited in these bands is radically different, due to their specific propagation behaviors and hardware characteristics. This paper reviews these differences and their implications, while dispelling common misunderstandings. Building on this foundation, we suggest appropriate signal processing schemes and use cases to efficiently exploit mMIMO in both frequency bands. * E. Björnson is with Linköping University, lower frequencies, which is achieved by mMIMO. There are, however, fundamental differences between how mMIMO technology can be designed, implemented, and exploited in sub-6 GHz and mmWave bands. In this paper, we provide a comparative overview, highlighting three main differences:1. The propagation channels build on the same physics, but basic phenomena such as diffraction, attenuation, and Fresnel zones are substantially different.2. The hardware implementation architecture changes with the increasing carrier frequency. More antennas can be integrated into a given area, but the insertion losses, intrinsic power-overhead in radio-frequency (RF) generation, and amplification result in diminishing gains.3. The signal processing algorithms depend on propagation and hardware. Channel estimation is resource-demanding at sub-6 GHz, while beamforming is straightforward. Conversely, mmWave channel estimation and beamforming are theoretically simpler since there are fewer propagation paths, but become challenging if hybrid beamforming is used.In the remainder of this article, we elaborate on these differences, including that they manifest how sub-6 GHz and mmWave bands are to be exploited to target different use-cases in 5G and beyond. Difference I: The propagation channelAn understanding of the electromagnetic propagation is crucial when considering mMIMO systems and frequencies up to mmWave bands. The channels behave fundamentally different from what we are used to in cellular networks, which exposes weaknesses in the channel modeling simplifications conventionally made.

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Zero-IF receivers are gaining interest because they enable low-cost WLAN OFDM terminals. However, Zero-IF receivers introduce IQ imbalance which may have a huge impact on the performance. Rather than increasing component cost to decrease the IQ imbalance, an alternative is to tolerate the IQ imbalance and compensate it digitally. Current solutions converge too slow for bursty WLAN communication. Moreover, the tremendous impact of a frequency offset on the IQ estimation/compensation problem is not considered. In this paper, we analyze the joint IQ-CFO estimation/compensation and propose a low-cost, highly effective compensation scheme. For large IQ imbalance ( = 10%, ∆φ = 10 o ) and large frequency offset, our solution results in an average remaining degradation below 0.5 dB compared to the reference case without IQ imbalance or frequency offset. It therefore enables the design of low-cost, low-complexity WLAN OFDM receivers.

Abstract-Orthogonal frequency-division multiplexing with cyclic prefix enables low-cost frequency-domain mitigation of multipath distortion. However, to determine the equalizer coefficients, knowledge of the channel frequency response is required. While a straightforward approach is to measure the response to a known pilot symbol sequence, existing literature reports a significant performance gain when exploiting the frequency correlation properties of the channel. Expressing this correlation by the finite delay spread, we build a deterministic model parametrized by the channel impulse response and, based on this model, derive the maximum-likelihood channel estimator. In addition to being optimal (up to the modeling error), this estimator receives an elegant time-frequency interpretation. As a result, it has a significantly lower complexity than previously published methods.Index Terms-Equalizers, orthogonal frequency-division multiplexing (OFDM).

Nowadays, a lot of effort is spent on developing inexpensive orthogonal frequency-division multiplexing (OFDM) receivers. Especially, zero intermediate frequency (zero-IF) receivers are very appealing, because they avoid costly IF filters. However, zero-IF front-ends also introduce significant additional front-end distortion, such as IQ imbalance. Moreover, zero-IF does not solve the phase noise problem. Unfortunately, OFDM is very sensitive to the receiver nonidealities IQ imbalance and phase noise. Therefore, we developed a new estimation/compensation scheme to jointly combat the IQ imbalance and phase noise at baseband. In this letter, we describe the algorithms and present the performance results. Our compensation scheme eliminates the IQ imbalance based on one OFDM symbol and performs well in the presence of phase noise. The compensation scheme has a fast convergence and a small residual degradation: even for large IQ imbalance, the overall system performance for an OFDM-wireless local area network (WLAN) case study is within 0.6 dB of the optimal case. As such, our approach greatly relaxes the mismatch specifications and thus enables low-cost zero-IF receivers.