Shahram Yousefi

Binary Soliton-Like Rateless Coding for the Y-Network

For the binary erasure channel, Luby Transform (LT) and Raptors codes have been shown to achieve capacity by carefully designed degree distributions for multicasting scenarios. Generalizing fountain codes to multihop networks requires transport nodes to perform network coding (NC). However, if intermediate nodes perform decentralized NC blindly, the statistical properties imposed by the fountain code are lost, and thus, a Gaussian elimination decoder must be used at the sink at the cost of significant increase in complexity compared to a belief propagation (BP) decoder. Addressing this problem, in this paper, we propose a new protocol, namely Soliton-like rateless coding (SLRC), by exploiting the benefits of fountain coding and NC coding over a Y-network. Ensuring key properties of the fountain code are preserved; BP can be effectively applied when transport nodes perform NC. Additionally, the proposed coding protocol is resilient to nodes churn rates. The SLRC scheme is evaluated against buffer-and-forwarding, and the distributed LT (DLT) codes; SLRC exhibits a 5% reduction in overhead compared to the state of the art DLT code at high decoding success rates. Simulations show that the proposed scheme preserves the benefits of NC and fountain coding.

Low-complexity sphere decoding algorithm for quasi-orthogonal space-time block codes

Space-time codes can be decoded by the sphere decoding (SD) algorithm to reduce the complexity and retain maximum-likelihood (ML) performance. In this letter, the ML metric of quasi-orthogonal space-time block codes is written into two independent Euclidean norms, thus SD can be applied to each function independently. The new scheme reduces the complexity by at least 85% for systems with four or more transmit antennas, compared with the conventional SD algorithm.

Solving Multi-UAV Dynamic Encirclement via Model Predictive Control

In order for teams of unmanned aerial vehicles (UAVs) to collaborate and cooperate to perform challenging group tasks, intelligent and flexible control strategies are required. One of the complex behaviors required of a team of UAVs is dynamic encirclement, which is a tactic that can be employed for persistent surveillance and/or to neutralize a target by restricting its movement. This tactic requires a high level of cooperation such that the UAVs maintain a desired and proper encirclement radius and angular velocity around the target. In this paper, model predictive control (MPC) is used to model and implement controllers for the problem of dynamic encirclement. The linear and nonlinear control policies proposed in this paper are applied as a high-level controller to control multiple UAVs to encircle a desired target in simulations and real-time experiments with quadrotors. The nonlinear solution provides a theoretical analysis of the problem, while the linear control policy is used for real-time operation via a combination of MPC and feedback linearization applied to the nonlinear UAV system. The contributions of this paper lie in the implementation of MPC to solve the problem of dynamic encirclement of a team of UAVs in real time and the application of theoretical stability analysis to the problem.

Improved Low-Complexity Soliton-Like Network Coding for a Resource-Limited Relay

In this paper, we examine the marriage of Fountain coding and network coding (NC). Fountain codes are capacity achieving erasure codes designed for point-to-point transmissions. NC is a throughput-optimal data dissemination technique, but its high-complexity decoding makes it unattractive for applications where limited resources are available. In this paper, we consider Fountain network coding to take advantage of efficient fountain decoders. Protocols such as Soliton-like rateless coding (SLRC) have previously addressed this issue, yet the re-encoding at the relay is expensive while there is still room for improving the performance. Extending SLRC, we propose the Improved Soliton-like Rateless Coding (ISLRC) protocol, where the relay is designed to perform distribution shaping given limited resources. ISLRC preserves the same properties as SLRC, but also makes the aggregate degree distribution more efficient for Fountain decoding. We analyze ISLRCs degree distribution and perform an asymptotic error analysis for the case where resources are most scarce. The ISLRC scheme is compared against other existing schemes. Simulation results show that even under the worst-case scenario of ISLRC, better performance can be achieved compared to SLRC and other existing schemes.

Exact MIMO Zero-Forcing Detection Analysis for Transmit-Correlated Rician Fading

We analyze the performance of multiple input/multiple output (MIMO) communications systems employing spatial multiplexing and zero-forcing detection (ZF). The distribution of the ZF signal-to-noise ratio (SNR) is characterized when either the intended stream or interfering streams experience Rician fading, and when the fading may be correlated on the transmit side. Previously, exact ZF analysis based on a well-known SNR expression has been hindered by the noncentrality of the Wishart distribution involved. In addition, approximation with a central-Wishart distribution has not proved consistently accurate. In contrast, the following exact ZF study proceeds from a lesser-known SNR expression that separates the intended and interfering channel-gain vectors. By first conditioning on, and then averaging over the interference, the ZF SNR distribution for Rician-Rayleigh fading is shown to be an infinite linear combination of gamma distributions. On the other hand, for Rayleigh-Rician fading, the ZF SNR is shown to be gamma-distributed. Based on the SNR distribution, we derive new series expressions for the ZF average error probability, outage probability, and ergodic capacity. Numerical results confirm the accuracy of our new expressions, and reveal effects of interference and channel statistics on performance.

ℓ0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing

The goal of sparse linear hyperspectral unmixing is to determine a scanty subset of spectral signatures of materials contained in each mixed pixel and to estimate their fractional abundances. This turns into an l0 -norm minimization, which is an NP-hard problem. In this paper, we propose a new iterative method, which starts as an l1 -norm optimization that is convex, has a unique solution, converges quickly and iteratively tends to be an l0 -norm problem. More specifically, we employ the arctan function with the parameter σ ≥ 0 in our optimization. This function is Lipschitz continuous and approximates l1 -norm and l0 -norm for small and large values of σ, respectively. We prove that the set of local optima of our problem is continuous versus σ. Thus, by a gradual increase of σ in each iteration, we may avoid being trapped in a suboptimal solution. We propose to use the alternating direction method of multipliers (ADMM) for our minimization problem iteratively while increasing σ exponentially. Our evaluations reveal the superiorities and shortcomings of the proposed method compared to several state-of-the-art methods. We consider such evaluations in different experiments over both synthetic and real hyperspectral data, and the results of our proposed methods reveal the sparsest estimated abundances compared to other competitive algorithms for the subimage of AVIRIS cuprite data.

Improved finite-length Luby-transform codes in the binary erasure channel

Fountain codes were introduced to provide high reliability and scalability and low complexities for networks such as the Internet. Luby-transform (LT) codes, which are the first realisation of Fountain codes, achieve the capacity of the binary erasure channel (BEC) asymptotically and universally. Most previous work on single-layer Fountain coding targets the design via the right degree distribution. The left degree distribution of an LT code is left as a Poisson to protect the universality. For finite lengths, this is no longer an issue; thus, the authors focus is on designing better codes for the BEC at practical lengths. Their left degree shaping provides codes outperforming LT codes and all other competing schemes in the literature. At a bit error rate of 10−7 and packet length k = 256, their scheme provides a realised rate of 0.6 which is 23.5% higher than that of Sorensen et al.’s decreasing-ripple-size scheme.

Trapping Sets of Fountain Codes

The remarkable results of Fountain codes over the binary erasure channel (BEC) have motivated research on their practical implementation over noisy channels. Trapping sets are a phenomenon of great practical importance for certain graph codes on noisy channels. Although trapping sets have been extensively studied for low-density parity-check (LDPC) codes, to the best of our knowledge they have never been fully explored for Fountain codes. In this letter, we demonstrate that trapping sets are damaging to the realized rate and decoding cost of Fountain codes. Furthermore, we show that through trapping set detection we may combat these negative effects.

A family of concatenated network codes for improved performance with generations

Random network coding can be viewed as a single block code applied to all source packets. To manage the concomitant high coding complexity, source packets can be partitioned into generations; block coding is then performed on each set. To reach a better performance-complexity tradeoff, we propose a novel concatenated network code which mixes generations while retaining the desirable properties of generation-based coding. Focusing on the codes erasure performance, we show that the probability of successfully decoding a generation on erasure channels can increase substantially for any erasure rate. Using both analysis (for small networks) and simulations (for larger networks), we show how the codes parameters can be tuned to extract best performance. As a result, the probability of failing to decode a generation is reduced by nearly one order of magnitude.

Throughput Performance of Generation-Based Network Coding

Using generations to implement random linear network coding garners benefits such as reduced decoding complexity. However, these benefits can come at the expense of throughput. In this paper, we seek to understand and maximize throughput for generation-based network coding (GBNC). Motivated by the application of network coding to scalable multicast, we consider schemes which result in high probability of decoding success with minimal feedback. We show that the throughput performance of GBNC is highly dependent on the choice of coding parameters and that GBNC becomes advantageous only when the number of source packet exceeds a network-dependent threshold. Results for various network topologies lead to the formulation of throughput-motivated guidelines for the adoption of GBNC.