We consider joint subchannel and power allocation for an orthogonal frequency division multiple access (OFDMA)-based cognitive radio network. We formulate the downlink resource allocation problem as a spectral-footprint (bandwidth-power product) minimization problem under interference threshold at primary users, total power and quality of service constraints. The cognitive base station solves this non-convex mixed-integer programming problem iteratively by dividing it into a subchannel allocation master problem and power allocation subproblems. The subchannel assignment problem is solved by applying a modified Hungarian algorithm while the power allocation subproblems are solved by using Lagrangian techniques. Specifically, we propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. The performance of our spectral-footprint minimization technique is compared with the waterfilling power allocation.

We propose a novel scheme for delay-optimal scheduling in multi-user multi-relay cellular wireless networks. The cell area is divided into several sectors, each serviced by an individual relay station (RS). In order to have simultaneous transmissions by the users in neighbouring sectors, we assume that users of each individual sector use separate set of orthogonal channels to communicate with the RS and the base station (BS). Moreover, a separate orthogonal channel is shared among relays for transmission to the BS. For uplink communication, users are allowed to choose between two modes of transmission, namely, direct transmission mode and relayed transmission mode through a simple transmission mode selection algorithm. Users are allocated fractions of the time-slot for the first phase of transmission (from the users to the BS and the RSs) in a time-division multiple access (TDMA) fashion. For the second phase of transmission (from the RSs to the BS), each RS is allocated a fraction of the time-slot. We model the problem of end-to-end (e2e) delay-optimal scheduling as an infinite-horizon average reward Markov decision process (MDP) for users and relays in two separate stages. An online learning approach is then employed to solve the problem in a distributed manner for both users and relays in each phase of transmission. The proposed online stochastic learning solution converges to the optimal solution almost surely (with probability 1) under some realistic conditions. Simulation results show that the proposed approach outperforms the conventional scheduling schemes.

We consider fair delay-optimal user selection and power allocation for a relay-based cooperative wireless network. Each user (mobile station) has an uplink queue with heterogeneous packet arrivals and delay requirements. Our system model consists of a base station, a relay station, and multiple users working in a time-division multiplexing (TDM) fashion, where per-user queuing is employed at the relay station to make the analysis of such system tractable. We model the problem as an infinite-horizon average reward Markov decision problem (MDP) where the control actions are functions of the instantaneous channel state information (CSI) as well as the queue state information (QSI) at the mobile and relay stations. To address the challenge of centralized control and huge complexity of MDP problems, we introduce a distributive and low-complexity solution. A linear structure is employed which approximates the value function of the associated Bellman equation by the sum of per-node value functions. Our online stochastic value iteration solution converges to the optimal solution almost surely (with probability 1) under some realistic conditions. Simulation results show that the proposed approach outperforms the conventional delay-aware user selection and power allocation schemes.

An analytical framework for the link-level end-to-end (e2e) queueing performance evaluation in a multiuser wireless relay network with automatic repeat request (ARQ)-based error control is presented. The e2e system consisting of a base station, a relay station, and multiple users is modeled as a probabilistic tandem of two finite queues for the relay and each user. The transmissions from the users are scheduled in a time-division multiplexing (TDM) fashion, i.e., in each time-slot only one user is in tandem with the relay's buffer with a certain probability. To make the analysis of such system tractable, the finite buffer of the relay is decomposed into smaller non-overlapping portions, each corresponding to an individual user's packets (i.e., per-user queueing). Using the decomposed model, radio link-level performance measures such as e2e packet loss rate, e2e delay and throughput are obtained analytically and compared with simulation results. As an application of this model, a method of obtaining optimum values for selection probabilities to maximize e2e aggregate throughput subject to users' individual delay constraints is presented.

Co-space interference (CSI) mitigation is one of the main challenging issues in multiple input multiple output (MIMO) systems. Using beamformer in both transmit and receive sides is an approach to mitigate the CSI. In the MIMO systems employing orthogonal frequency division multiplexing (OFDM) technique, frequency domain or time domain beamforming can be used. In this paper, we propose a new time domain broadband beamforming (TBBF) for MIMO-OFDM systems based on a SVD type of block circular channel matrix. TBBF is attained by polynomial eigen-beamformer when the right and the left polynomial eigenvectors are utilized at the transmitter and the receiver sides, respectively. The new proposed TBBF is able to completely mitigate the CSI due to shaping the multiple beams in orthogonal directions. Performance of the proposed polynomial eigen-based TBBF is evaluated by computer simulations and compared with the previously proposed TBBF scheme based on the SBR2 algorithm. The results show that the proposed time domain beamforming method outperforms the SBR2-based scheme.