Yahya H. Ezzeldin

Best relay selection for underlay cognitive radio systems with collision probability minimization

We consider an underlay relay-assisted two-hop cognitive link which employs best relay selection to enhance the performance of secondary transmissions while keeping the interference at the primary receiver below a specified level. Best relay selection is implemented using countdown timers without cooperation between the relays. Assuming channel knowledge at the relays, the best relay is determined by the maximum signal-to-noise ratio (SNR) at the secondary receiver. Due to the timer-based operation, collisions may occur and the timer design is optimized to minimize the collision probability. The performances of the amplify-and-forward and decode-and-forward modes of operation are compared. Closed form expressions for the secondary outage probability are derived for both schemes. Numerical results provide insight into the system operation and how the outage probability depends on the various system parameters.

Efficiently finding simple schedules in Gaussian half-duplex relay line networks

The problem of operating a Gaussian Half-Duplex (HD) relay network optimally is challenging due to the exponential number of listen/transmit network states that need to be considered. Recent results have shown that, for the class of Gaussian HD networks with N relays, there always exists a simple schedule, i.e., with at most N+1 active states, that is sufficient for approximate (i.e., up to a constant gap) capacity characterization. This paper investigates how to efficiently find such a simple schedule over line networks. Towards this end, a polynomial-time algorithm is designed and proved to output a simple schedule that achieves the approximate capacity. The key ingredient of the algorithm is to leverage similarities between network states in HD and edge coloring in a graph. It is also shown that the algorithm allows to derive a closed-form expression for the approximate capacity of the Gaussian line network that can be evaluated distributively and in linear time.

Sparse reconstruction-based detection of spatial dimension holes in cognitive radio networks

In this paper, we investigate a spectrum-sensing algorithm for detecting spatial dimension holes in Multiple-Input Multiple-Output (MIMO) transmissions for OFDM systems using Compressive Sensing (CS) tools. This extends the energy detector to allow for detecting transmission opportunities even if the band is already energy filled. We show that the task described above is not performed efficiently by regular MIMO decoders (such as MMSE decoder) due to possible sparsity in the transmit signal. Since CS reconstruction tools take into account the sparsity order of the signal, they are more efficient in detecting the activity of the users. Building on successful activity detection by the CS detector, we show that the use of a CS-aided MMSE decoder yields better performance rather than using either CS-based or MMSE decoders separately.

Wireless network simplification: Beyond diamond networks

We consider an arbitrary layered Gaussian relay network with L layers of N relays each, from which we select subnetworks with K relays per layer. We prove that: (i) For arbitrary L;N and K = 1, there always exists a subnetwork that approximately achieves 2/(L-1)N+4 (resp. 2/LN+2) of the network capacity for odd L (resp. even L), (ii) For L = 2; N = 3; K = 2, there always exists a subnetwork that approximately achieves 1/2 of the network capacity. We also provide example networks where even the best subnetworks achieve exactly these fractions (up to additive gaps). Along the way, we derive some results on MIMO antenna selection and capacity decomposition that may also be of independent interest.

PNCR: A physical network coding framework for routing in wireless ad-hoc networks

In this paper, PNCR, a routing framework based on Physical Network Coding is proposed. PNCR enables shared utilization of the channel during the broadcast and multiple access phases of transmissions. These additional transmission opportunities can be observed as performance gains in terms of less packet delays and better network throughput. In this paper, we discuss the details of our proposed framework to utilize the virtues presented by Physical Network Coding. Existing work on Physical Network Coding is theoretic and does not present itself as a tool for multi-hop network deployment except for limited network topologies. Our work in this paper is an effort to utilize such techniques for networks of arbitrary structure. We evaluate the performance of our proposed framework using simulations and show that it achieves better throughput and less delivery delay per packet than conventional wireless routing solutions without Physical Network Coding.

Pseudo-Lattice Treatment for Subspace Aligned Interference Signals

In this paper, we propose a channel transformation technique for joint decoding of desired and interfering signals in an interference alignment scenario consisting of K users with multiple antennas. Our technique, i.e., pseudo-lattice treatment, is based on the compute-and-forward framework. Our technique can be implemented to provide decoding gains with low complexity for subspace aligned interference signals. We evaluate the performance of the system through simulations that show the performance gain attained by using our pseudo-lattice method over subspace decoding techniques for interference alignment in lowand medium-SNR conditions.