Abstract-The high processing complexity of data detection in the large-scale multiple-input multiple-output (MIMO) uplink necessitates high-throughput VLSI implementations. In this paper, we propose-to the best of our knowledge-first matrix inversion implementation suitable for data detection in systems having hundreds of antennas at the base station (BS). The underlying idea is to carry out an approximate matrix inversion using a small number of Neumann-series terms, which allows one to achieve near-optimal performance at low complexity. We propose a novel VLSI architecture to efficiently compute the approximate inverse using a systolic array and show reference FPGA implementation results for various system configurations. For a system where 128 BS antennas receive data from 8 single-antenna users, a single instance of our design processes 1.9 M matrices/s on a Xilinx Virtex-7 FPGA, while using only 3.9% of the available slices and 3.6% of the available DSP48 units. I. INTRODUCTIONMultiple-input multiple-output (MIMO) combined with spatial multiplexing [1] is the key technology in most modern wireless communication standards, such as 3GPP LTE or IEEE 802.11n. MIMO technology offers improved link reliability and higher data rates compared to single-antenna systems by simultaneously transmitting multiple data streams in the same frequency band. However, conventional point-to-point and multi-user (MU) MIMO wireless systems already start to approach the theoretical throughput limits. Consequently, novel transmission technologies become necessary to meet the ever-growing demand for higher data rates without further increasing the communication bandwidth [2], [3].Large-scale MIMO (or massive MIMO) is an emerging technology, which uses antenna arrays having orders of magnitude more elements at the base station (BS) compared to conventional (small-scale) MIMO systems, while simultaneously serving a small number of users in the same frequency band [2]. This technology promises further improvements in spectral efficiency and link reliability over conventional (small-scale) MIMO systems [3], [4]. In addition, large-scale MIMO has the potential to reduce the operational power consumption at the BS [2], [5].Unfortunately, the benefits of large-scale MIMO come at the cost of significantly increased computational complexity in the BS compared to small-scale MIMO systems. Specifically, data detection in the large-scale MIMO uplink is among the most critical tasks, as the presence of hundreds of antennas at the BS requires novel detection algorithms that scale favorably to high-dimensional problems. Since optimal methods, such as maximum-likelihood (ML) detection or sphere
Cooperation among cognitive radios for spectrum sensing is deemed essential for environments with deep shadows. In this paper, we study cooperative spectrum sensing for cognitive radio ad hoc networks where there is no fusion center to aggregate the information from various secondary users. We propose a novel consensus-inspired cooperative sensing scheme based on linear iterations that is fully distributed and low-cost. In addition, the trade-offs on the number of consensus iterations are explored for scenarios with different shadow fading characteristics. Furthermore, we model Insistent Spectrum Sensing Data Falsification (ISSDF) attack aimed at consensus-based iterative schemes and show its destructive effect on the cooperation performance which accordingly results in reduced spectrum efficiency and increased interference with primary users. We propose a trust management scheme to mitigate these attacks and evaluate the performance improvement through extensive Monte Carlo simulations for large-scale cognitive radio ad hoc networks in TV white space. Our proposed trust management reduces the harm of a set of collusive ISSDF attackers up to two orders of magnitude in terms of missed-detection and false alarm error rates. Moreover, in a hostile environment, integration of trust management into cooperative schemes considerably relaxes the sensitivity requirements on the cognitive radio devices.Index Terms-Dynamic spectrum access, Cognitive radio ad hoc networks, Distributed consensus-based cooperative spectrum sensing, Trust management, Insistent spectrum sensing data falsification attack
In a cognitive radio ad hoc network, secondary users must cooperate in a decentralized way in order to determine the presence or absence of the primary user. In such a setting, malicious nodes deteriorate the cooperative spectrum sensing performance by reporting incorrect sensing information to the other nodes. We classify distributed cooperative spectrum sensing in cognitive radio ad hoc networks into two categories: consensusbased and non-consensus-based. We investigate and compare the sensitivity of these categories to spectrum sensing data falsification attacks and analyze the benefit of trust management in enhancing the performance of these methods. To this end, we introduce a novel trust-aware gossip-based scheme for distributed sensing. Our simulation results show that the proposed scheme significantly improves the cooperative sensing performance in the presence of malicious nodes in the network.
Abstract-To comply with the evolving wireless standards, base stations must provide greater data rates over the serial data link between base station processor and RF unit. This link is especially important in distributed antenna systems and cooperating base stations settings. This paper explores the compression of baseband signal samples prior to transfer over the above-mentioned link. We study lossy and lossless compression of baseband signals and analyze the cost and gain of each approach. Sample quantizing is proposed as a lossy compression scheme and it is shown to be effective by experiments. With QPSK modulation, sample quantizing achieves a compression ratio of 4:1 and 3.5:1 in downlink and uplink, respectively. The corresponding compression ratios are 2.3:1 and 2:1 for 16-QAM. In addition, lossless compression algorithms including arithmetic coding, Elias-gamma coding, and unused significant bit removal, and also a recently proposed baseband signal compression scheme are evaluated. The best compression ratio achieved for lossless compression is 1.5:1 in downlink. Our over-the-air experiments suggest that compression of baseband signal samples is a feasible and promising solution for increasing the effective bit rates of the link to/from remote RF units without requiring much complexity and cost to the base station.
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