Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas $M$ than scheduled users $K$ because the users’ channels are likely to be near-orthogonal when $M/K > 10$ . However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users $K^\star$ depends on $M$ and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of $K^\star$ in the large- $M$ regime is derived in closed form, while simulations are used to show what happens at finite $M$ , in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal $M/K$ is less than 10 in many cases of practical relevance. Interestingly, $K^\star$ depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same $K$ .

Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?

In certain communication systems where information is to be transmitted from one point to another, additional side information is available at the transmitting point. This side information relates to the state of the transmission channel and can be used to aid in the coding and transmission of information. In this paper a type of channel with side information is studied and its capacity determined.

Channels with Side Information at the Transmitter

The exponential increase in mobile data traffic is considered to be a critical driver towards the new era, or 5G, of mobile wireless networks. 5G will require a paradigm shift that includes very high carrier frequency spectra with massive bandwidths, extreme base station densities, and unprecedented numbers of antennas to support the enormous increase in the volume of traffic. This paper discusses several design choices, features, and technical challenges that illustrate potential research topics and challenges for the future generation of mobile networks. This article does not provide a final solution but highlights the most promising lines of research from the recent literature in common directions for the 5G project. The potential physical layer technologies that are considered for future wireless communications include spatial multiplexing using massive multi-user multiple-input multiple-output (MIMO) techniques with millimetre-waves (mm-waves) in small cell geometries. These technologies are discussed in detail along with the areas for future research.

Evolution towards fifth generation (5G) wireless networks: Current trends and challenges in the deployment of millimetre wave, massive MIMO, and small cells

Resource-aware relay selection for inter-cell interference avoidance in 5G heterogeneous network for Internet of Things systems

5G-air-simulator: An open-source tool modeling the 5G air interface

Massive MIMO interference coordination for 5G broadband access: Integration and system level study

In the FP7 project METIS we investigate suitable combinations of massive MIMO, joint transmission coordinated multipoint and small cells for future 5G systems. Challenging is a tight integration over small and macro layers. Straight forward is to ensure orthogonality between small and macro cell layers by allocation to different RF frequency bands like e.g. 2.6 and 3.5GHz respectively. Here within the macro layer frequency band an opportunistic tight cooperation between macro and small cell radio stations is being proposed. This requires dual band small cell radio stations operating in a traditional local area frequency band like 3.5GHz for data off loading and simultaneously - and on a need basis - at e.g. 2.6GHz as performance booster for macro UEs. First high level evaluations indicate significant spectral efficiency, capacity and coverage gains for macro cell users benefiting from higher Rx power and rank enhancement.

Opportunistic CoMP for 5G massive MIMO Multilayer Networks

One of the essential challenges for MIMO or massive MIMO and cooperative multipoint systems is obtaining accurate channel state information (CSI) at the base station side, as the related closed loop MIMO precoders are sensitive to channel aging effects. For that reason, channel prediction is often seen as one of the main enablers to maintain a large part of the performance gains achievable with ideal CSI. It has been claimed that Kalman filtering provides an upper bound for channel prediction performance. In line with this, for real world radio channels alternative channel prediction methods based on parameter estimation achieved comparably worse prediction results. Here, we propose a so called profiling solution as a novel parameter estimation method, which promises to improve the prediction horizon by a factor of two to three compared to Kalman filtering based on autoregressive models. This is indicated by first evaluations based on a real world radio channel measurement in the NOKIA campus in Munich.

Profiling of mobile Radio Channels

Massive multiple input multiple output (MIMO) transmission and coordinated multipoint transmission are candidate technologies for increasing data throughput in evolving 5G standards. Frequency division duplex (FDD) is likely to remain predominant in large parts of the spectrum below 6 GHz for future 5G systems. Therefore, it is important to estimate the downlink FDD channels from a very large number of antennas, while avoiding an excessive downlink reference signal overhead. We here propose and investigate a three part solution. First, massive MIMO downlinks use a fixed grid of beams. For each user, only a subset of beams will then be relevant, and require estimation. Second, sets of coded reference signal sequences, with cyclic patterns over time, are used. Third, each terminal estimates its most relevant channels. We here propose and compare a linear mean square estimation and a Kalman estimation. Both utilize frequency and antenna correlation, and the later also utilizes temporal correlation. In extensive simulations, this scheme provides channel estimates that lead to an insignificant beamforming performance degradation as compared to full channel knowledge. The cyclic pattern of coded reference signals is found to be important for reliable channel estimation, without having to adjust the reference signals to specific users.

Low-Overhead Cyclic Reference Signals for Channel Estimation in FDD Massive MIMO

Joint transmission coordinated multipoint (JT CoMP) has been identified as a potential differentiator for future 5G radio systems due to its superior interference mitigation capabilities. Further, the combination with massive multiple-input-multiple-output (mMIMO) when a set of fixed grid of beams is used at eNodeB results in sparse overall channel matrices with a relatively low number of relevant channel components, which reduces the feedback overhead for reporting of channel state information (CSI). Although JT CoMP faces several challenges from synchronization to CSI outdating, in this paper the focus will be on complexity reduction of the precoding over large clustered cells utilizing massive MIMO. It will be derived how the typical sparse number of relevant channel components per user equipment can be exploited to reduce the number of floating point operations (FLOPs) by a factor of ten compared to state-of-the-art solutions for the calculation of the Moore Penrose pseudo inverse of the channel matrix.

Wolfgang Zirwas

Papers

Massive MIMO interference coordination for 5G broadband access: Integration and system level study

Opportunistic CoMP for 5G massive MIMO Multilayer Networks

Profiling of mobile Radio Channels

Low-Overhead Cyclic Reference Signals for Channel Estimation in FDD Massive MIMO

Low complexity Moore-Penrose inverse for large CoMP areas with sparse massive MIMO channel matrices