Efficient detectors for MIMO-OFDM systems under spatial correlation antenna arrays

This work analyzes the performance of implementable detectors for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system conditions, including antenna correlation and array configuration. A time-domain channel model was used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order, and antenna array configurations. Several MIMO-OFDM detectors were analyzed for the purpose of achieving high performance combined with high capacity systems and manageable computational complexity. Numerical Monte Carlo simulations demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic-based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM context.     

Performance analysis of a single-user MISO ultra-wideband time reversal system with DFE

This paper presents an analysis of the performance of a baseband multiple-input single-output (MISO) time reversal ultra-wideband system (TR-UWB) incorporating a symbol spaced decision feedback equalizer (DFE). A semi-analytical performance analysis based on a Gaussian approach is considered, which matched well with simulation results, even for the DFE case. The channel model adopted is based on the IEEE 802.15.3a model, considering correlated shadowing across antenna elements. In order to provide a more realistic analysis, channel estimation errors are considered for the design of the TR filter. A guideline for the choice of equalizer length is provided. The results show that the system’s performance improves with an increase in the number of transmit antennas and when a symbol spaced equalizer is used with a relatively small number of taps compared to the number of resolvable paths in the channel impulse response. Moreover, it is possible to conclude that due to the time reversal scheme, the error propagation in the DFE does not play a role in the system’s performance.     

Health promoting properties of protein hydrolysates produced from oil palm (Elaeis guineensis) kernel

This study aimed to determine the lipid-lowering properties, antioxidant capacity (AC) and angiotensin-I converting enzyme (ACE)-inhibitory activity of oil palm kernel protein hydrolysates (OPKHs) that were produced using protease and pepsin-pancreatin hydrolysis. The effects of the OPKHs on apolipoprotein B (apoB) secretion was assessed using HepG2 cells as a model and the AC of the OPKHs was determined based on ABTS radical scavenging activity and ferric reducing antioxidant power (FRAP). Both protease and pepsin-pancreatin hydrolysates reduced apoB secretion significantly (p<0.05). The OPKHs scavenged ABTS radicals effectively and demonstrated a high reducing power even at a low concentration (1 mg/mL). The AC of the OPKHs was significantly correlated with the OPKHs protein content. However, the OPKHs demonstrated very low ACE-inhibitory activity. The pepsinpancreatin hydrolysate demonstrated significant lipidlowering properties, favourable AC and ACE inhibitory activity in compared to protease hydrolysate. Therefore, OPKH demonstrate the potential as a nutraceutical for functional foods.     

Massive MIMO pilot assignment optimization based on total capacity

We investigate the effects of pilot assignment in multi-cell massive multiple-input multiple-output systems. When deploying a large number of antennas at base station (BS), and linear detection/precoding algorithms, the system performance in both uplink (UL) and downlink (DL) is mainly limited by pilot contamination. This interference is proper of each pilot, and thus system performance can be improved by suitably assigning the pilot sequences to the users within the cell, according to the desired metric. We show in this paper that UL and DL performances constitute conflicting metrics, in such a way that one cannot achieve the best performance in UL and DL with a single pilot assignment configuration. Thus, we propose an alternative metric, namely total capacity, aiming to simultaneously achieve a suitable performance in both links. Since the PA problem is combinatorial, and the search space grows with the number of pilots in a factorial fashion, we also propose a low complexity suboptimal algorithm that achieves promising capacity performance avoiding the exhaustive search. Besides, the combination of our proposed PA schemes with an efficient power control algorithm unveils the great potential of the proposed techniques in providing improved performance for a higher number of users. Our numerical results demonstrate that with 64 BS antennas serving 10 users, our proposed method can assure a 95%-likely rate of 4.2 Mbps for both DL and UL, and a symmetric 95%-likely rate of 1.4 Mbps when serving 32 users.     

Efficient detection in uniform linear and planar arrays MIMO systems under spatial correlated channels

In this paper, the efficiency of various multiple‐input multiple‐output (MIMO) detectors was analyzed from the perspective of highly correlated channels, where MIMO systems have a lack of performance, besides in some cases, an increasing complexity. Considering this hard but a useful scenario, various MIMO detection schemes were accurately evaluated concerning complexity and bit error rate performance. Specifically, successive interference cancellation, lattice reduction, and the combination of them were associated with conventional linear MIMO detection techniques. To demonstrate effectiveness, a wide range of the number of antennas and modulation formats have been considered aiming to verify the potential of such MIMO detection techniques according to their performance‐complexity trade‐off. We have also studied the correlation effect when both transmit and receiver sides are equipped with uniform linear array and uniform planar array antenna configurations. The performance of different detectors is carefully compared when both antenna array configurations are deployed considering a different number of antennas and modulation order, especially under near‐massive MIMO condition. We have also discussed the relationship between the array factor and the bit error rate performance of both antenna array structures.     

Low-Complexity Massive MIMO Detectors Under Spatial Correlation and Channel Error Estimates

Wireless Personal Communications - The performance, complexity and effectiveness of various massive MIMO (M-MIMO) detectors are analyzed operating under highly spatial correlated uniform planar...     

LR-Aided MIMO Detectors under Correlated and Imperfectly Estimated Channels

In this contribution, lattice reduction (LR) technique is applied to improve the multiple-input multiple-output (MIMO) detector performance under correlated channels and imperfect channel estimation constrains. Zero-forcing, minimum mean squared error, ordered successive interference cancellation and sphere decoding (SD) detectors are analysed taking into consideration (a) different correlated fading channel indexes, (b) increasing spectral efficiency, by combining number of transmit antennas and modulation formats, and (c) channel coefficient error estimations. Analysis of correlated channel effects over the MIMO system performance equipped with different LR-aided detectors are carried out, indicating the robustness of those detectors and the SD–MIMO detector deficiency to deal with such channel condition. Besides, computational complexities are compared aiming to determine the best LR–MIMO detection scheme under the perspective of performance-complexity tradeoff.     

MapReduce scheduling algorithms: a review

Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas of big data. The widespread popularity of big data processing platforms using MapReduce framework is the growing demand to further optimize their performance for various purposes. In particular, enhancing resources and jobs scheduling are becoming critical since they fundamentally determine whether the applications can achieve the performance goals in different use cases. Scheduling plays an important role in big data, mainly in reducing the execution time and cost of processing. This paper aims to survey the research undertaken in the field of scheduling in big data platforms. Moreover, this paper analyzed scheduling in MapReduce on two aspects: taxonomy and performance evaluation. The research progress in MapReduce scheduling algorithms is also discussed. The limitations of existing MapReduce scheduling algorithms and exploit future research opportunities are pointed out in the paper for easy identification by researchers. Our study can serve as the benchmark to expert researchers for proposing a novel MapReduce scheduling algorithm. However, for novice researchers, the study can be used as a starting point.

     

Nonnegative matrix factorization and metamorphic malware detection

Metamorphic malware change their internal code structure by adopting code obfuscation technique while maintaining their malicious functionality during each infection. This causes change of their signature pattern across each infection and makes signature based detection particularly difficult. In this paper, through static analysis, we use similarity score from matrix factorization technique called Nonnegative Matrix Factorization for detecting challenging metamorphic malware. We apply this technique using structural compression ratio and entropy features and compare our results with previous eigenvector-based techniques. Experimental results from three malware datasets show this is a promising technique as the accuracy detection is more than 95%.