Range-Angle-Dependent Beamforming by Frequency Diverse Array Antenna

This paper proposes a range-angle-dependent beamforming for frequency diverse array (FDA) antenna systems. Unlike conventional phased-array antenna, the FDA antenna employs a small amount of frequency increment compared to the carrier frequency across the array elements. The use of frequency increment generates an antenna pattern that is a function of range, time and angle. The range-angle-dependent beamforming allows the FDA antenna to transmit energy over a desired range or angle. This provides a potential to suppress range-dependent clutter and interference which is not accessible for conventional phased-array systems. In this paper, a FDA radar signal model is formed and the range-angle-dependent beamforming performance is examined by analyzing the transmit/receive beampatterns and the output signal-to-interference-plus-noise ratio (SINR) performance. Extensive simulation examples and results are provided

Performance of WLAN in Downlink MU-MIMO Channel with the Least Cost in Terms of Increased Delay

To improve the performance of IEEE 802.11 wireless local area (WLAN) networks, different frame-aggregation algorithms are proposed by IEEE 802.11n/ac standards to improve the throughput performance of WLANs. However, this improvement will also have a related cost in terms of increasing delay. The traffic load generated by mixed types of applications in current modern networks demands different network performance requirements in terms of maintaining some form of an optimal trade-off between maximizing throughput and minimizing delay. However, the majority of existing researchers have only attempted to optimize either one (to maximize throughput or minimize the delay). Both the performance of throughput and delay can be affected by several factors such as a heterogeneous traffic pattern, target aggregate frame size, channel condition, competing stations, etc. However, under the effect of uncertain conditions of heterogeneous traffic patterns and channel conditions in a network, determining the optimal target aggregate frame size is a significant approach that can be controlled to manage both throughput and delay. The main contribution of this study was to propose an adaptive aggregation algorithm that allows an adaptive optimal trade-off between maximizing system throughput and minimizing system delay in the WLAN downlink MU-MIMO channel. The proposed approach adopted different aggregation policies to adaptively select the optimal aggregation policy that allowed for achieving maximum system throughput by minimizing delay. Both queue delay and transmission delay, which have a significant impact when frame-aggregation algorithms are adopted, were considered. Different test case scenarios were considered such as channel error, traffic pattern, and number of competing stations. Through systemlevel simulation, the performance of the proposed approach was validated over the FIFO aggregation algorithm and earlier adaptive aggregation approaches, which only focused on achieving maximum throughput at the expense of delay. The performance of the proposed approach was evaluated under the effects of heterogenous traffic patterns for VoIP and video traffic applications, channel conditions, and number of STAs for WLAN downlink MU-MIMO channels

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

This paper investigates an adaptive frame aggregation technique in the medium access control (MAC) layer for the Wireless Local Area Network (WALN) downlink Multi-User–Multiple-In Multiple-Out (MU-MIMO) channel. In tackling the challenges of heterogeneous traffic demand among spatial streams, we proposed a new adaptive aggregation algorithm which has a superior performance over the baseline First-in–First-Out (FIFO) scheme in terms of system throughput performance and channel utilization. However, this earlier work does not consider the effects of wireless channel error. In addressing the limitations of this work, this study contributes an enhanced version of the earlier model considering the effect of channel error. In this approach, a dynamic adaptive aggregation selection scheme is proposed by employing novel criteria for selecting the optimal aggregation policy in WLAN downlink MU-MIMO channel. Two simulation setups are conducted to achieve this approach. The simulation setup in Step 1 performs the dynamic optimal aggregation policy selection strategy as per the channel condition, traffic pattern, and number of stations in the network. Step 2 then performed the optimal wireless frame construction that would be transmitted in the wireless channel in adopting the optimal aggregation policy obtained from Step 1 that maximizes the system performance. The proposed adaptive algorithm not only achieve the optimal system throughput in minimizing wasted space channel time but also provide a good performance under the effects of different channel conditions, different traffic models such as Pareto, Weibull, and fBM, and number of users using the traffic mix of VoIP and video data. Through system-level simulation, our results again show the superior performance of our proposed aggregation mechanism in terms of system throughput performance and space channel time compared to the baseline FIFO aggregation approach

A New Adaptive Frame Aggregation Method for Downlink WLAN MU-MIMO Channels

Accommodating the heterogeneous traffic demand among streams in the downlink MU-MIMO channel is among the challenges that affect the transmission efficiency since users in the channel do not always have the same traffic demand. Consequently, it is feasible to adjust the frame size to maximize the system throughput. The existing adaptive aggregation solutions do not consider the effects of different traffic scenarios and they use a Poison traffic model which is inadequate to represent the real network traffic scenarios, thus leading to suboptimal solutions. In this study, we propose some adaptive aggregation strategies which employ a novel dynamic adaptive aggregation policy selection algorithm in addressing the challenges of heterogenous traffic demand in the downlink MU-MIMO channel. Different traffic models are proposed to emulate real world traffic scenarios in the network and to analyze the proposed aggregation polices with respect to various traffic models. Finally, through simulation, we demonstrate the performance of our adaptive algorithm over the baseline FIFO aggregation approach in terms of system throughput performance and channel utilization in achieving the optimal frame size of the system

Frequency Diverse Array MIMO Radar Adaptive Beamforming with Range-Dependent Interference Suppression in Target Localization

Conventional multiple-input and multiple-output (MIMO) radar is a flexible technique which enjoys the advantages of phased-array radar without sacrificing its main advantages. However, due to its range-independent directivity, MIMO radar cannot mitigate nondesirable range-dependent interferences. In this paper, we propose a range-dependent interference suppression approach via frequency diverse array (FDA) MIMO radar, which offers a beamforming-based solution to suppress range-dependent interferences and thus yields much better DOA estimation performance than conventional MIMO radar. More importantly, the interferences located at the same angle but different ranges can be effectively suppressed by the range-dependent beamforming, which cannot be achieved by conventional MIMO radar. The beamforming performance as compared to conventional MIMO radar is examined by analyzing the signal-to-interference-plus-noise ratio (SINR). The Cramér-Rao lower bound (CRLB) is also derived. Numerical results show that the proposed method can efficiently suppress range-dependent interferences and identify range-dependent targets. It is particularly useful in suppressing the undesired strong interferences with equal angle of the desired targets

Molecular Characterization and Antimicrobial Susceptibility of Nasal Staphylococcus aureus Isolates from a Chinese Medical College Campus

Staphylococcus aureus colonization and infection occur more commonly among persons living or working in crowded conditions, but characterization of S. aureus colonization within medical communities in China is lacking. A total of 144 (15.4%, 144/935) S. aureus isolates, including 28 (3.0%, 28/935) MRSA isolates, were recovered from the nares of 935 healthy human volunteers residing on a Chinese medical college campus. All S. aureus isolates were susceptible to vancomycin, quinupristin/dalfopristin and linezolid but the majority were resistant to penicillin (96.5%), ampicillin/sulbactam (83.3%) and trimethoprim/sulfamethoxazole (93.1%). 82%, (23/28) of the MRSA isolates and 66% (77/116) of the MSSA isolates were resistant to multiple antibiotics, and 3 MRSA isolates were resistant to mupirocin—an agent commonly used for nasal decolonization. 16 different sequence types (STs), as well as SCCmec genes II, III, IVd, and V, were represented among MRSA isolates. We also identified, for the first time, two novel STs (ST1778 and ST1779) and 5 novel spa types for MRSA. MRSA isolates were distributed in different sporadic clones, and ST59-MRSA-VId- t437 was found within 3 MRSA isolates. Moreover, one isolate with multidrug resistance belonging to ST398-MRSA-V- t571 associated with animal infections was identified, and 3 isolates distributed in three different clones harbored PVL genes. Collectively, these data indicate a high prevalence of nasal MRSA carriage and molecular heterogeneity of S. aureus isolates among persons residing on a Chinese medical college campus. Identification of epidemic MRSA clones associated with community infection supports the need for more effective infection control measures to reduce nasal carriage and prevent dissemination of MRSA to hospitalized patients and health care workers in this community

MIMO Antenna Array Design with Polynomial Factorization

One of the main advantages of multiple-input multiple-output (MIMO) antenna is that the degrees-of-freedom can be significantly increased by the concept of virtual antenna array, and thus the MIMO antenna array should be carefully designed to fully utilize the virtual antenna array. In this paper, we design the MIMO antenna array with the polynomial factorization method. For a desired virtual antenna array, the polynomial factorization method can optimally design the specified MIMO transmitter and receiver. The array performance is examined by analyzing the degrees-of-freedom and statistical output signal-to-interference-plus-noise ratio (SINR) performance. Design examples and simulation results are provided

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

This paper investigates an adaptive frame aggregation technique in the medium access control (MAC) layer for the Wireless Local Area Network (WALN) downlink Multi-User–Multiple-In Multiple-Out (MU-MIMO) channel. In tackling the challenges of heterogeneous traffic demand among spatial streams, we proposed a new adaptive aggregation algorithm which has a superior performance over the baseline First-in–First-Out (FIFO) scheme in terms of system throughput performance and channel utilization. However, this earlier work does not consider the effects of wireless channel error. In addressing the limitations of this work, this study contributes an enhanced version of the earlier model considering the effect of channel error. In this approach, a dynamic adaptive aggregation selection scheme is proposed by employing novel criteria for selecting the optimal aggregation policy in WLAN downlink MU-MIMO channel. Two simulation setups are conducted to achieve this approach. The simulation setup in Step 1 performs the dynamic optimal aggregation policy selection strategy as per the channel condition, traffic pattern, and number of stations in the network. Step 2 then performed the optimal wireless frame construction that would be transmitted in the wireless channel in adopting the optimal aggregation policy obtained from Step 1 that maximizes the system performance. The proposed adaptive algorithm not only achieve the optimal system throughput in minimizing wasted space channel time but also provide a good performance under the effects of different channel conditions, different traffic models such as Pareto, Weibull, and fBM, and number of users using the traffic mix of VoIP and video data. Through system-level simulation, our results again show the superior performance of our proposed aggregation mechanism in terms of system throughput performance and space channel time compared to the baseline FIFO aggregation approach