Active Attack on User Load Achieving Pilot Design in Massive MIMO Networks

In this paper, we propose an active attacking strategy on a massive multiple-input multiple-output (MIMO) network, where the pilot sequences are obtained using the user load-achieving pilot sequence design. The user load-achieving design ensures that the signal-to-interference-plus-noise ratio (SINR) requirements of all the users in the massive MIMO networks are guaranteed even in the presence of pilot contamination. However, this design has some vulnerabilities, such as one known pilot sequence and the correlation among the pilot sequences, that may be exploited by active attackers. In this work, we first identify the potential vulnerabilities in the user load-achieving pilot sequence design and then, accordingly, develop an active attacking strategy on the network. In the proposed attacking strategy, the active attackers transmit known pilot sequences in the uplink training and artificial noise in the downlink data transmission. Our examination demonstrates that the per-cell user load region is significantly reduced by the proposed attacking strategy. As a result of the reduced per-cell user load region, the SINR requirements of all the users are no longer guaranteed in the presence of the active attackers. Specifically, for the worst affected users the SINR requirements may not be ensured even with infinite antennas at the base station.Comment: Accepted in IEEE GlobeCOM 201

Harmonized Cellular and Distributed Massive MIMO: Load Balancing and Scheduling

Multi-tier networks with large-array base stations (BSs) that are able to operate in the "massive MIMO" regime are envisioned to play a key role in meeting the exploding wireless traffic demands. Operated over small cells with reciprocity-based training, massive MIMO promises large spectral efficiencies per unit area with low overheads. Also, near-optimal user-BS association and resource allocation are possible in cellular massive MIMO HetNets using simple admission control mechanisms and rudimentary BS schedulers, since scheduled user rates can be predicted a priori with massive MIMO. Reciprocity-based training naturally enables coordinated multi-point transmission (CoMP), as each uplink pilot inherently trains antenna arrays at all nearby BSs. In this paper we consider a distributed-MIMO form of CoMP, which improves cell-edge performance without requiring channel state information exchanges among cooperating BSs. We present methods for harmonized operation of distributed and cellular massive MIMO in the downlink that optimize resource allocation at a coarser time scale across the network. We also present scheduling policies at the resource block level which target approaching the optimal allocations. Simulations reveal that the proposed methods can significantly outperform the network-optimized cellular-only massive MIMO operation (i.e., operation without CoMP), especially at the cell edge

Group-blind detection with very large antenna arrays in the presence of pilot contamination

Massive MIMO is, in general, severely affected by pilot contamination. As opposed to traditional detectors, we propose a group-blind detector that takes into account the presence of pilot contamination. While sticking to the traditional structure of the training phase, where orthogonal pilot sequences are reused, we use the excess antennas at each base station to partially remove interference during the uplink data transmission phase. We analytically derive the asymptotic SINR achievable with group-blind detection, and confirm our findings by simulations. We show, in particular, that in an interference-limited scenario with one dominant interfering cell, the SINR can be doubled compared to non-group-blind detection.Comment: 5 pages, 4 figure