Energy limitation is an adverse problem in designing routing protocols for underwater sensor networks (UWSNs). To prolong the network lifetime with limited battery power, an energy balanced and efficient routing protocol, called energy balanced and lifetime extended routing protocol (EBLE), is proposed in this paper. The proposed EBLE not only balances traffic loads according to the residual energy, but also optimizes data transmissions by selecting low-cost paths. Two phases are operated in the EBLE data transmission process: (1) candidate forwarding set selection phase and (2) data transmission phase. In candidate forwarding set selection phase, nodes update candidate forwarding nodes by broadcasting the position and residual energy level information. The cost value of available nodes is calculated and stored in each sensor node. Then in data transmission phase, high residual energy and relatively low-cost paths are selected based on the cost function and residual energy level information. We also introduce detailed analysis of optimal energy consumption in UWSNs. Numerical simulation results on a variety of node distributions and data load distributions prove that EBLE outperforms other routing protocols (BTM, BEAR and direct transmission) in terms of network lifetime and energy efficiency.
Spectrum auctions can motivate the legacy (primary) spectrum owners to lease their idle spectrum to other secondary spectrum users and eventually improve the spectrum utilization. Compared with traditional auctions, a significant difference of spectrum auctions is the spatial reusability, which means several buyers can use the same channel if they are far away from each other such that they will not interfere with each other. The challenge is how to exploit spatial reusability to improve spectrum utilization while keeping the auctions economicrobust (truthful in particular). In this paper, we propose TDSA, an efficient truthful double spectrum auction design to solve this problem, which has a novel virtual group bidding mechanism to improve spectrum utilization as well as a unique pricing strategy to guarantee truthfulness. We prove that our proposal is truthful. Experimental results show that our proposal can efficiently exploit spatial reusability of spectrum to achieve high spectrum utilization in most cases.
Cognitive radio is able to share the spectrum with primary licensed user, which greatly improves the spectrum efficiency. We study the optimal power allocation for cognitive radio to maximize its ergodic capacity under interference outage constraint. An optimal power allocation scheme for the secondary user with complete channel state information is proposed and its approximation is presented in closed form in Rayleigh fading channels. When the complete channel state information is not available, a more practical transmitter-side joint access ratio and transmit power constraint is proposed. The new constraint guarantees the same impact on interference outage probability at primary user receiver. Both the optimal power allocation and transmit rate under the new constraint are presented in closed form. Simulation results evaluate the performance of proposed power allocation schemes and verify our analysis.
A reduced K-best sphere decoding (K-best SD) algorithm for Multiple-Input Multiple-Output (MIMO) detection is proposed. The algorithm reduces the complexity of the K-best SD by combining the statistics character of the signal and the requirement of the quality of service (QoS). In the reducing processing of the proposed algorithm, the chi-square distribution (CSD) property of the signal, the optimal symbol error rate (SER) property and the loss of pruning are considered together to give a theoretic error bound and then a threshold to determined which route can be pruned to reduced the calculation complexity. The algorithm reduces the complexity with a controllable cost of performance decrease. Simulation results on a 16QAM system with 4×4 antennas show that the algorithm can attain the near-optimal performance with a significant complexity reduction comparing to the original K-best SD or maximum likelihood (ML) algorithm.
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