A delay-enhanced maximum weight scheduler (DMWS) for wireless Ad hoc networks is presented with single-hop traffic flows and general interference models. By introducing a novel set of weights for all wireless links in the process of scheduling, the proposed DMWS is proved to achieve a tight upper bound of the expected delay with regard to the traffic loading factor under arbitrary network topologies. The throughput optimality of DMWS is also validated, i.e., DMWS guarantees the maximum capacity region. Simulation results verify our analysis and the performance of DMWS.

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This paper presents a traffic management framework for multi-channel wireless backbone network (WBN). To date, WBNs are not allowed to flexibly use multiple channels even if there are many vacant channels. Furthermore, the limited available channel(s) are not utilized effectively. Therefore, we propose a virtual AP enabling to handle the unlimited number of channels potentially, and then present a traffic control framework with OpenFlow, which freely controls traffic on multiple channels. Experimental results show that WBN linearly increases the network capacity according to the increase in the number of channels. That is, effective utilization of multi-channel can be achieved.

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In this letter, we present a new method that combines the spectrum spreading technique and FDTD (Finite-Difference Time-Domain) simulation for reducing the computation time in multiple-antenna analysis. In this method, signals for the multiple antennas are generated by multiplying non-identical spreading codes, and multiple antennas are excited at the same time. This approach means that only a single FDTD simulation is needed whereas the conventional method needs as many simulations as there are transmitting antennas. A 2×2 multiple-antenna is simulated by way of a demonstration and it is found that the results of the proposed method agree well with those of the conventional method even though its computation time is shorter than that of the conventional method.

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In this letter, we investigate the channel estimation for single-input multiple-output systems, where the channel vector and the noise spatial covariance matrix (SCM) are jointly estimated. By utilizing the inherent relationship of the received data symbols’ sample covariance matrix, the SCM and the channel vector, we propose a conditional maximum likelihood (CML) estimator, which is the solution of a non-convex optimization problem. The global optimum can be expressed in a quasi-closed-form with one unknown scalar parameter, which can be efficiently identified via solving polynomial equations. Simulations show that the proposed CML estimator achieves significant performance gains compared with the traditional maximum likelihood estimator, as long as the data symbol number is not too small.

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There is a problem with OFDM (orthogonal frequency division multiplexing) in that high sidelobes arise from the discontinuity of adjacent OFDM symbols. Although the spectrum sculpting precoder (SSP) produces smaller sidelobes than plain OFDM, a correction symbol inserted to suppress sidelobes causes degradation in the SER performance. This paper therefore proposes applying SLM (selected mapping) to SSP for reducing the power of the correction symbols. Our method allows improving the SER performance.

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