-Within the wide range of issues related to 4G wireless networks, due to the scope of the ISSTA conference, in this presentation we highlight some segments of wireless networks that will be based on spread spectrum (including UWB) physical layer and mainly discuss only network adaptability and reconfigurability issues.

This paper presents a hierarchical hypothesis test and a feature-based blind modulation classification (BMC) algorithm for linearly modulated signals. The proposed BMC method is based on the combination of elementary cumulant (EC) and cyclic cumulants. The EC is used to decide whether the constellations are from real, circular, or rectangular class, which is referred to as macro classifier. The cyclic cumulant is used to classify modulation within a subclass, which is referred to as micro classifier. For the micro classification, we use positions of nonzero cyclic frequencies (symbol rate frequency or carrier frequency) of the received signals. A hierarchical hypothesis-based theoretical framework has been developed to find the probability of error for the proposed classification. The method works over a flat fading channel without any knowledge of the signal parameters. The proposed method is more robust than the one based on EC and at the same time it requires lower complexity than the maximum likelihood approach. To validate the proposed scheme, measurement is carried out in realistic scenarios. The performance of the new algorithm is compared with the existing methods. In this paper, we have considered a six-class problem including binary phase-shift keying, quadrature phase-shift keying (QPSK), offset-QPSK, π/4-QPSK, minimum shift keying, and 16-quadrature amplitude modulation.

The important problem of weighted sum rate maximization (WSRM) in a multicellular environment is intrinsically sensitive to channel estimation errors. In this paper, we study ways to maximize the weighted sum rate in a linearly precoded multicellular downlink system where the receivers are equipped with a single antenna. With perfect channel information available at the base stations, we first present a novel fast converging algorithm that solves the WSRM problem. Then, the assumption is relaxed to the case where the error vectors in the channel estimates are assumed to lie in an uncertainty set formed by the intersection of finite ellipsoids. As our main contributions, we present two procedures to solve the intractable nonconvex robust designs based on the worst case principle. The proposed iterative algorithms solve the semidefinite programs in each of their steps and provably converge to a locally optimal solution of the robust WSRM problem. The proposed approaches are numerically compared against each other to ascertain their robustness towards channel estimation imperfections. The results clearly indicate the performance gain compared to the case when channel uncertainties are ignored in the design process. For certain scenarios, we also quantify the gap between the proposed approximations and exact solutions.

Abstract-A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

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Abstract-This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and in which mobile devices are enabled with device-to-device communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station or by connecting through another UE that acts as an access point and relays the traffic from a cellular base station. The paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multi-cell scenarios, in which the optimum solution cannot be easily known a priori, the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power, while exhibiting much lower computational requirements.

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