Joint Channel Estimation and Pilot Allocation in Underlay Cognitive MISO Networks

Cognitive radios have been proposed as agile technologies to boost the spectrum utilization. This paper tackles the problem of channel estimation and its impact on downlink transmissions in an underlay cognitive radio scenario. We consider primary and cognitive base stations, each equipped with multiple antennas and serving multiple users. Primary networks often suffer from the cognitive interference, which can be mitigated by deploying beamforming at the cognitive systems to spatially direct the transmissions away from the primary receivers. The accuracy of the estimated channel state information (CSI) plays an important role in designing accurate beamformers that can regulate the amount of interference. However, channel estimate is affected by interference. Therefore, we propose different channel estimation and pilot allocation techniques to deal with the channel estimation at the cognitive systems, and to reduce the impact of contamination at the primary and cognitive systems. In an effort to tackle the contamination problem in primary and cognitive systems, we exploit the information embedded in the covariance matrices to successfully separate the channel estimate from other users' channels in correlated cognitive single input multiple input (SIMO) channels. A minimum mean square error (MMSE) framework is proposed by utilizing the second order statistics to separate the overlapping spatial paths that create the interference. We validate our algorithms by simulation and compare them to the state of the art techniques.Comment: 6 pages, 2 figures, invited paper to IWCMC 201

Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little inter-user interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and inter-user interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/massive-MIMO-hardware-impairment

Robust Pilot Decontamination Based on Joint Angle and Power Domain Discrimination

We address the problem of noise and interference corrupted channel estimation in massive MIMO systems. Interference, which originates from pilot reuse (or contamination), can in principle be discriminated on the basis of the distributions of path angles and amplitudes. In this paper we propose novel robust channel estimation algorithms exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approaches are able to cope with a wide range of system and topology scenarios, including those where, unlike in previous works, interference channel may overlap with desired channels in terms of multipath angles of arrival or exceed them in terms of received power. In particular we establish analytically the conditions under which the proposed channel estimator is fully decontaminated. Simulation results confirm the overall system gains when using the new methods.Comment: 14 pages, 5 figures, accepted for publication in IEEE Transactions on Signal Processin

Does Massive MIMO Fail in Ricean Channels?

Massive multiple-input multiple-output (MIMO) is now making its way to the standardization exercise of future 5G networks. Yet, there are still fundamental questions pertaining to the robustness of massive MIMO against physically detrimental propagation conditions. On these grounds, we identify scenarios under which massive MIMO can potentially fail in Ricean channels, and characterize them physically, as well as, mathematically. Our analysis extends and generalizes a stream of recent papers on this topic and articulates emphatically that such harmful scenarios in Ricean fading conditions are unlikely and can be compensated using any standard scheduling scheme. This implies that massive MIMO is intrinsically effective at combating interuser interference and, if needed, can avail of the base-station scheduler for further robustness.Comment: IEEE Wireless Communications Letters, accepte