Due to its wide range of applications, distributed beamforming (DB) has become one of the most popular study subjects in recent years. The critical problem of detecting distributed beamforming in achieving the carrier phase alignment at the receiver end. This paper presents an iterative algorithm that uses trial and error (TE) learning to attain phase alignment between the sending nodes that only requires 1-bit feedback from the receiver. This algorithm fits our case best, as the convergence speed is gained by obtaining the feedback information more competently than current solutions and improving the received signal strength (RSS) on the receiving end.

Multicore architectures are mainstream due to ever increasing demand of throughput by modern applications. However, the suboptimal utilization of available resources in these architectures may imply an inevitable energy overhead. This energy overhead can only be avoided if the multicore systems support reconfiguration of available resources as per application demand. To achieve the target objectives (i.e., Energy efficiency with Throughput maximization) in multicore systems, many decision variables need to be optimized or analyzed to find the better trade-off. Heuristic-based approaches are aimed to provide a good-enough solution instead of a lengthy exhaustive search. This paper presents an Evolutionary Algorithm (EA)-based approach, i.e., Nondominated Sorting Genetic Algorithm-II (NSGA-II). Three decision variables, i.e., number of cores, cache size and frequency are used to find best solution. The proposed approach is validated over a set of parallel benchmarks using a cycle accurate simulator. The results show a significant amount of energy saving along with minimal impact on the throughput of the system.

In this paper, we study a weighted-sum rate maximization problem in SC-FDMA which is adopted as the multiple access scheme for the uplink in the 3GPP-LTE standard. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel polynomial-complexity optimization framework that is inspired from the recently developed canonical duality theory. We first formulate the resource allocation problem as a binary-integer programming (BIP) problem and then transform the BIP problem into a continuous space canonical dual problem which is a concave maximization problem. Based on the solution of the continuous space dual problem, we propose a resource allocation algorithm that has polynomial complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature to assess its performance. Simulation results show that the proposed algorithm improves the system performance significantly.

In this paper, we consider the challenging problem of power allocation in interfering wireless networks with services having stringent delay constraints. The objective is to allocate optimally the power to each user, take advantage of the time diversity of the time varying channel and satisfy the delay constraints of the services. To this end, we model the bit rate dynamics of the users as a linear stochastic control scheme and formulate a risk-sensitive control framework that minimizes an exponential cost function of the deviation between the traffic arrivals and departures from the queues. We provide the optimal solution of the power control problem and provide simulation results to assess the superiority of our solution compared to some existing solutions.

In this paper, we study sum-power minimization problem in SC-FDMA which is adopted as the multiple access scheme for uplink in the 3GPP-LTE standard. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel polynomial-complexity optimization framework that is inspired from the recently developed canonical duality theory. We first formulate the resource allocation problem as a binary-integer programming (BIP) problem and then transform the BIP problem into a continuous space canonical dual problem which is a concave maximization problem under certain conditions. Based on the solution of the canonical dual problem, we derive a joint power and sub-channels allocation algorithm that has polynomial complexity. We provide conditions under which the proposed algorithm is optimal. The proposed framework is illustrated through simulations where the results show that the proposed algorithm improves the system performance significantly.

In this paper, we study resource allocation in a downlink OFDMA system assuming imperfect channel state information (CSI) at the transmitter. To achieve the individual QoS of the users in OFDMA system, adaptive resource allocation is very important, and has therefore been an active area of research. However, in most of the the previous work perfect CSI at the transmitter is assumed which is rarely possible due to channel estimation error and feedback delay. In this paper, we study the effect of channel estimation error on resource allocation in a downlink OFDMA system. We assume that each user terminal estimates its channel by using an MMSE estimator and sends its CSI back to the base station through a feedback channel. We approach the problem by using convex optimization framework, provide an explicit closed form expression for the users' transmit power and then develop an optimal margin adaptive resource allocation algorithm. Our proposed algorithm minimizes the total transmit power of the system subject to constraints on users' average data rate. The algorithm has polynomial complexity and solves the problem with zero optimality gaps. Simulation results show that our algorithm highly improves the system performance in the presence of imperfect channel estimation.

Most of existing works on resource allocation in TDMA and OFDMA systems assume the availability of perfect channel state information (CSI) at the transmitter. The availability of only a noisy estimate of the channel and the feedback delay are the problems which are faced more often. In this paper, we study the effect of feedback delay on resource allocation in a downlink OFDMA system. We approach the problem by using convex optimization framework and get the optimal solution with zero optimality gap. By considering the probability distribution function of the current CSI conditioned on outdated CSI in the resource allocation framework, we propose an algorithm that minimizes the total transmit power of the system subject to strict constraints on users' conditional expected capacities. Results show that the system performance is sensitive to feedback delay and provide guidelines for system dimensioning and design.