Performance Improvement Of IEEE Research Articles

Combining contention‐based access and dynamic service period allocation for performance improvement in IEEE 802.11ad mmWave WLAN

SummaryIn IEEE 802.11ad millimeter wave wireless LANs, the directional multi‐giga bit stations (DMGSTAs) use contention‐based access periods (CBAPs) and scheduled service periods (SPs) for medium access. The STAs carrying non‐QoS traffic use CBAPs, while SPs are allocated for STAs carrying high QoS applications. During CBAP, the STAs use enhanced distributed channel access (EDCA) scheme. Further, 802.11ad advocates dynamic allocation of SP for guaranteed data transmission based on a centralized polling scheme. The standard advocates that the coverage area around the access point can be divided into several sectors, and the total CBAP shall be divided among the sectors on a time sharing basis. The STAs residing within a sector simultaneously contend during the CBAP fraction of that sector. However, such STAs have to defer their transmission attempts and wait for the designated CBAP fraction of the succeeding beacon interval (BI), if the residual time in the current CBAP fraction is not sufficient for a frame transmission. This leads to very high delay and reduced throughput. The objective of this paper is to propose an efficient hybrid medium access control scheme, where the deferred STAs during CBAP are scheduled again by utilizing the unallocated slots in the SP of the same BI. We describe an analytical model for the throughput and the average frame delay, under the proposed scheme, and compare the performance against the legacy scheme, where the STAs use CBAP alone for channel access. The analytical and simulation results establish that the proposed scheme significantly improves the throughput and reduces the average frame delay.

Adaptive Backoff Algorithm for Contention Window for Dense IEEE 802.11 WLANs

The performance improvement in IEEE 802.11 WLANs in widely fluctuating network loads is a challenging task. To improve the performance in this saturated state, we develop an adaptive backoff algorithm that maximizes the system throughput, reduces the collision probability, and maintains a high fairness for the IEEE 802.11 DCF under dense network conditions. In this paper, we present two main advantages of the proposed ABA-CW algorithm. First, it estimates the number of active stations and then calculates an optimal contention window based on the active station number. Each station calculates the channel state probabilities by observing the channel for the total backoff period. Based on these channel states probabilities, each station can estimate the number of active stations in the network, after which it calculates the optimal CW utilizing the estimated active number of stations. To evaluate the proposed mechanism, we derive an analytical model to determine the network performance. From our results, the proposed ABA-CW mechanism achieved better system performance compared to fixed-CW (BEB, EIED, LILD, and SETL) and adaptive-CW (AMOCW, Idle Sense) mechanisms. The simulation results confirmed the outstanding performance of the proposed mechanism in that it led to a lower collision probability, higher throughput, and high fairness.

의료 WBAN 환경을 위한 IEEE 802.15.4 MAC 성능 개선

의료 WBAN 환경을 위한 IEEE 802.15.4 MAC 성능 개선

Performance Improvement of IEEE 802.22 WRAN Physical Layer

The spectrum available for the wireless services is limited, the increased demand of wireless application has put a lot of limitations on the utilization of available radio spectrum. For the efficient spectrum utilization for wireless application IEEE 802.22 standard i.e. WRAN (Wireless Regional Area Network) is developed which is based on cognitive radio technique that senses the free available spectrum. It allows sharing of geographically unused channels allocated to the TV Broadcast Service, without interference. In this paper we are evaluating the performance of WRAN over physical layer with QPSK, 16-QAM and 64-QAM modulation with Convolution coding with code rate of 1/2, 2/3, 3/4, 5/6 and obtaining the BER curves for rician channel. Simulation is performed in MATLAB. Keywords - CC, CP, CR, OFDMA, PHY Layer, WRAN