Enabling the spectrum handoff for multimedia applications in cognitive radio networks (CRNs) is challenging, due to multiple interruptions from primary users (PUs), contentions among secondary users (SUs), and heterogenous Quality-of-Experience (QoE) requirements. In this paper, we propose a learning-based and QoE-driven spectrum handoff scheme to maximize the multimedia users' satisfaction. We develop a mixed preemptive and non-preemptive resume priority (PRP/NPRP) M/G/1 queueing model for modeling the spectrum usage behavior for prioritized multimedia applications. Then, a mathematical framework is formulated to analyze the performance of SUs. We apply the reinforcement learning to our QoE-driven spectrum handoff scheme to maximize the quality of video transmissions in the long term. The proposed learning scheme is asymptotically optimal, model-free, and can adaptively perform spectrum handoff for the changing channel conditions and traffic load. Experimental results demonstrate the effectiveness of the proposed queueing model for prioritized traffic in CRNs, and show that the proposed learning-based QoE-driven spectrum handoff scheme improves quality of video transmissions.

LT codes are capacity achieving and flexible rateless codes that need a single-bit feedback to inform the encoder of the successful decoding. However, this weak feedback channel remains unused when the transmission is in progress. In addition, although LT codes are asymptotically capacity achieving, their performance significantly degrades for short block lengths. Consequently, we propose LT Codes with Alternating Feedback (LT-AF Codes) that considerably improve the performance of LT codes for short-block lengths when belief propagation decoder is in use. In our proposed scheme, the decoder alternatively issues two types of feedbacks based on the dependencies of the still undecoded received output symbols and the number of decoded input symbols. We propose two methods to form the latter type of feedback with a trade-off in their complexity and performance.

Existing rateless codes have low intermediate symbol recovery rates (ISRR). Therefore, we first design new rateless codes with close to optimal ISRR employing genetic algorithms. Next, we assume an estimate of the channel erasure rate is available and propose an algorithm to further improve the ISRR of the designed codes.

In distributed rateless coding, multiple disjoint sources need to deliver their rateless coded symbols (where a symbol may contain a single bit or thousands of bits) to a common destination via a single relay. In this paper, we propose and design novel distributed rateless codes called DU-rateless codes that can provide Unequal Error Protection (UEP) for disjoint sources with unequal data lengths on erasure channels. To design DU-rateless codes, we tune the coding parameters at each source and propose to smartly combine the encoded symbols at the relay. We analyze DU-rateless codes employing And-Or tree analysis technique and leverage our analysis to design several sets of codes for various setups employing the-state-of-the-art multi-objective genetic algorithms. We evaluate the performance of the designed codes using numerical simulations and discuss their advantages.

In this paper, we propose CStorage a fully distributed and efficient data storage scheme for wireless sensor networks (WSNs) based on compressive sensing (CS) techniques. CStorage requires much smaller number of transmissions compared to existing algorithms by exploiting the compressibility of the natural signals along with the broadcast property of wireless channels. In CStorage, after the probabilistic readings dissemination phase, each node obtains one compressed sample (measurement) of the network's readings. Later, a data collector can query a small number measurements and recover all sensors' readings employing CS. We find the optimal parameters of CStorage and evaluate its performance.

When multiple sources of data need to transmit their rateless coded symbols through a single relay to a common destination, a distributed rateless code instead of several separate conventional rateless codes can be employed to encode the input symbols to increase the transmission efficiency and flexibility. In this paper, we propose distributed rateless codes (DU-rateless) that can provide unequal error protection (UEP) for distributed sources with different data block lengths and different importance levels. We analyze our proposed DU-rateless code employing And-Or tree analysis technique. Next, we design several sets of optimum DU-rateless codes for various setups employing multi-objective genetic algorithms and evaluate their performances.

LT codes are modern and efficient rateless forward error correction (FEC) codes with close to channel capacity performance. Nevertheless, in intermediate range where the number of received encoded symbols is less than the number of source symbols, LT codes have very low recovery rates. In this paper, we propose a novel algorithm which significantly increases the intermediate recovery rate of LT codes, while it preserves the codes' close to channel capacity performance. To increase the intermediate recovery rate, our proposed algorithm rearranges the transmission order of the encoded symbols exploiting their structure, their transmission history, and an estimate of the channel's erasure rate. We implement our algorithm for conventional LT codes, and numerically evaluate its performance.

In this paper, we design several degree distributions for rateless codes with optimum intermediate packet recovery rates. In rateless coding, the employed degree distribution significantly affects the packet recovery rate. Each degree distribution is designed based on the number of message packets, k, and desired coding overhead, ¿, which is the ratio of the number of received packets, n, to k, i.e., ¿ = n/k. Previously designed degree distributions are tuned for full recovery of the entire source packets for ¿'s slightly larger than 1, and as a consequence, they show very small packet recovery rates for ¿ < 1. Hence, finding degree distributions with maximal packet recovery rates in intermediate range, 0 < ¿ < 1, is of interest. We define packet recovery rates at three values of ¿ as our conflicting objective functions and employ NSGA-II multiobjective genetic algorithms optimization method to find several degree distributions with optimum packet recovery rates. We propose degree distributions for both cases of finite and infinite (asymptotic) k.