Abstract We consider an unslotted primary channel with alternating on/off activity and provide a solution to the problem of finding the optimal secondary transmission power and duration given the sensing outcome. The goal is to maximize a weighted sum of the primary and secondary throughput where the weight is determined by the degree of protection and the minimum rate required by... We consider an unslotted primary channel with alternating on/off activity and provide a solution to the problem of finding the optimal secondary transmission power and duration given the sensing outcome. The goal is to maximize a weighted sum of the primary and secondary throughput where the weight is determined by the degree of protection and the minimum rate required by the primary terminals. Two sensing schemes are considered: perfect sensing in which the actual state of the primary channel is revealed, and soft sensing in which the secondary transmission power and time are determined based on the sensing metric directly. We use an upper bound for the secondary throughput assuming that the secondary receiver tracks the instantaneous secondary channel state information. We justify the upper bound on informationtheoretic grounds and also provide a lower bound on secondary throughput. The weighted sum throughput objective function is non-convex and, hence, the optimal solution is obtained via exhaustive search. Our results show enhanced throughput by allowing the secondary to transmit even when the channel is found to be busy. The enhancement increases as the channel gain between the secondary transmitter and primary receiver decreases. For the examined system parameter values, the throughput gain from soft sensing depends on the “distance” between the likelihood functions of the received primary signal at the secondary transmitter. Further investigation is needed to quantify the potential of soft sensing. Read More Read Less

Abstract The paper proposes an analytical method for determining basic traffic characteristics of systems servicing multi-rate overflow traffic streams generated by a finite number of sources. In particular, we describe an optimum solution for the implementation of telephony system. Analytical results of blocking probability calculated using the presented methodology has been compared with the data obtained from the system simulation process.... The paper proposes an analytical method for determining basic traffic characteristics of systems servicing multi-rate overflow traffic streams generated by a finite number of sources. In particular, we describe an optimum solution for the implementation of telephony system. Analytical results of blocking probability calculated using the presented methodology has been compared with the data obtained from the system simulation process. Read More Read Less

Abstract A GPS based tracking system is proposed which keeps track of the location and speed of a vehicle which can be monitored by a mobile phone text messaging system. The system is able to provide real-time text alerts for speed and current location of a vehicle. Particularly, the present location can be locked and the system will alert the owner... A GPS based tracking system is proposed which keeps track of the location and speed of a vehicle which can be monitored by a mobile phone text messaging system. The system is able to provide real-time text alerts for speed and current location of a vehicle. Particularly, the present location can be locked and the system will alert the owner by sending a text message if the vehicle is moved from the present locked location. In addition, the speed can be locked and an alert can be sent to owner’s mobile phone if this speed is exceeded. Read More Read Less

Abstract In communications, compressive sensing is largely accepted for sparse channel estimation and its variants. The key advantage of compressed sensing lies in its reconstruction algorithm recovering the original high-dimensional sparse data from low dimensional data. However, the algorithms will be in week effectiveness when the sparsity is unknown. In this article, a novel sparse adaptive channel estimation algorithm based on... In communications, compressive sensing is largely accepted for sparse channel estimation and its variants. The key advantage of compressed sensing lies in its reconstruction algorithm recovering the original high-dimensional sparse data from low dimensional data. However, the algorithms will be in week effectiveness when the sparsity is unknown. In this article, a novel sparse adaptive channel estimation algorithm based on discrete Fourier transform (DFT) was proposed. It performed preliminary estimation by using discrete Fourier transform matrix as the observation matrix and inverse discrete Fourier transform to the received data at the receiver. It is found that the local peak indexes of this preliminary estimation results correspond with the non-zero taps of the sparse channel. After the non-zero taps were located, least square (LS) algorithm was used to estimate the channel impulse response (CIR). Mathematical analysis and simulation results have shown that the proposed algorithm (discrete Fourier transform based-least square, DFT-LS) outperform than existed multiple tracking algorithms such as sparsity adaptive matching pursuit (SAMP) and orthogonal matching pursuit (OMP), and are with lower complexity as well. Read More Read Less