Publications

This paper presents an unusual view of interference wireless networks based on complex system thinking. To proceed with this analysis, a literature review of the different applications of complex systems is firstly presented to illustrate how such an approach can be used in a wide range of research topics, from economics to linguistics. Then the problem of quantifying the fundamental limits of wireless systems where the co-channel interference is the main limiting factor is described and hence contextualized in the perspective of complex systems. Specifically some possible internal and external pressures that the network elements may suffer are identified as, for example, queue stability, maximum packet loss rate and transmit power constraint. Besides, other important external factors such as mobility and incoming traffic are also pointed out. As a study case, a decentralized point-to-point interference network is described and several claims about the optimal design setting for different network states and under two mobility conditions, namely quasi-static and highly mobile, are stated based on results found in the literature. Using these claims as a background, the design of a robust adaptive algorithm that each network element should run is investigated.

We consider a multi-operator small cell network where mobile network operators are sharing a common pool of radio resources. The goal is to ensure long term fairness of spectrum sharing without coordination among small cell base stations. It is assumed that spectral allocation of the small cells is orthogonal to the macro network layer, and thus, only the small cell traffic is modeled. We develop a decentralized control mechanism for base stations using the Gibbs sampling based learning technique, which allocates a suitable amount of spectrum for each base station. Five algorithms are compared addressing co-primary multi-operator resource sharing under heterogeneous traffic requirements and the performance is assessed through extensive system-level simulations. The main performance metrics are user throughput and fairness between operators. The numerical results demonstrate that the proposed Gibbs sampling based learning algorithm provides about tenfold cell edge throughput gains compared to state-of-the-art algorithms, while ensuring fairness between operators.

This paper studies combined relay selection and opportunistic geographic routing strategies for autonomous wireless sensor networks where transmissions occur over multiple hops. The proposed solution is built upon three constituent parts: (i) relay selection algorithm, (ii) contention resolution mechanism, and (iii) geographic forwarding strategy. Using probability generating function and spatial point process as the theoretic background, we propose an auction-based algorithm for selecting the relay node that relies on the network topology as side-information. Our results show that geographic solutions that iteratively exploit the local knowledge of the topology to ponder the algorithm operation outperforms established random approaches.

The problem of throughput optimization in decentralized wireless networks with spatial randomness under queue stability and packet loss constraints is investigated in this paper. Two key performance measures are analyzed, namely the effective link throughput and the network spatial throughput. Specifically, the tuple of medium access probability, coding rate, and maximum number of retransmissions that maximize each throughput metric is analytically derived for a class of Poisson networks, in which packets arrive at the transmitters following a geometrical distribution. Necessary conditions so that the effective link throughput and the network spatial throughput are stable and achievable under bounded packet loss are determined, as well as upper bounds for both cases by considering the unconstrained optimization problem. Our results show in which system configuration stable achievable throughput can be obtained as a function of the network density and the arrival rate. They also evince conditions for which the per-link throughput-maximizing operating points coincide or not with the aggregate network throughput-maximizing operating regime.

The millimeter wave (mmWave) communication uses directional antennas. Hence, achieving fine alignment of transmit and receive beams at the initial access phase is quite challenging and time-consuming. In this paper, we provide a dynamic-weight based beam sweeping direction and synchronization signal block (SSB) allocation algorithm to optimize the cell search of the initial access in mmWave 5G NR networks. The number of SSBs transmitted in each beam sweeping direction depends on previously learned experience which is based on the number of detected UEs (user equipment) per SSB for each sweeping direction. Overall, numerical simulation results indicate that the proposed algorithm is shown to be capable of detecting more users with a lower misdetection probability. Furthermore, it is possible to achieve the same performance with a smaller number of dynamic resource (i.e., SSB) allocation, compared to constant resource allocation.

Radio frequency (RF) wireless power transfer (WPT) is an appealing technology to provide sustainable and cost-efficent power supply to low-power devices in future wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is a serious challenge for practical applications. The key contributing factors include channel losses, transceivers’ power consumption, and losses from components such as the digital-to-analog converter (DAC), high-power amplifier (HPA), and rectenna. Careful consideration of these factors is essential for optimizing PTE, which is the focus of this research. Herein, we consider a fully connected hybrid multi-antenna transmit architecture that aims to charge non-linear energy harvesters. First, we present a mathematical framework to determine the harvested power from multi-tone signal transmissions and the system’s power consumption. Then, we formulate a joint waveform and analog beamforming design problem to minimize system’s power consumption and fulfill user’s charging needs. With this in place, and due to the problem high-complexity, we propose a particle swarm optimization (PSO)-based solution. Moreover, we also model the problem as a Markov decision process and propose a solution based on deep deterministic policy gradient (DDPG). Numerical results demonstrate that the proposed algorithms converge to suboptimal solutions. Moreover, simulation results show that system power consumption reduces with lower DAC and phase shifter resolution, as well as increased antenna length. Conversely, power consumption rises with the number of users and RF chains. Notably, across all these scenarios, PSO-JWB outperforms DDPG-JWB, requiring lower overall system power consumption.

We model the problem of infrastructure sharing among mobile network operators (MNOs) as a multiple-seller single-buyer market where the MNOs are able to share their own base stations (BSs) with each other. First, we use techniques from stochastic geometry to find the coverage probability of the infrastructure sharing system and analyze the trade-off between increasing the transmit power of a BS and the BS intensity of a buyer MNO required to achieve a given quality-of-service (QoS) in terms of the coverage probability. We also analyze the power consumption of the network per unit area (i.e., areal power consumption) and show that it is a piecewise continuous function composed of a linear and a convex functions. We show that when the transmit power of the BSs and/or the BS intensity of a network increases, the system becomes interference limited and the coverage probability tends to saturate at a certain value. As such, when the required QoS is set above this bound, an MNO can improve its coverage by buying infrastructure from other MNOs. Subsequently, we analyze the strategy of a buyer MNO on choosing how many MNOs and which MNOs to buy the infrastructure from. The optimal strategy of the buyer is given by greedy fractional knapsack algorithm. On the sellers' side, the pricing and the fraction of infrastructure to be sold are formulated using a Cournot oligopoly game.

The conflicting problems of growing mobile service demand and underutilization of dedicated spectrum has given rise to a paradigm where mobile network operators (MNOs) share their infrastructure among themselves in order to lower their operational costs, while at the same time increase the usage of their existing network resources. We model and analyze such an infrastructure sharing system considering a single buyer MNO and multiple seller MNOs. Assuming that the locations of the BSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability for a user served by the buyer MNO in an infrastructure sharing environment. We analyze the trade-off between increasing the transmit power of a base station (BS) and the intensity of BSs owned by the buyer MNO required to achieve a given quality-of-service (QoS) in terms of the SINR coverage probability. Also, for a seller MNO, we analyze the power consumption of the network per unit area (i.e., areal power consumption) which is shown to be a piecewise continuous function of BS intensity, composed of a linear and a convex function. Accordingly, the BS intensity of the seller MNO can be optimized to minimize the areal power consumption while achieving a minimum QoS for the buyer MNO. We then use these results to formulate a single-buyer multiple-seller BS infrastructure market. The buyer MNO is concerned with finding which seller MNO to purchase from and what fraction of BSs to purchase. On the sellers' side, the problem of pricing and determining the fraction of infrastructure to be sold is formulated as a Cournot oligopoly market. We prove that the iterative update of each seller's best response always converges to the Nash Equilibrium.

Thanks to the smart device revolution, modern wireless devices have increased computational/storage capabilities and can also support for multiple network interfaces such as cellular and WiFi interfaces. Intelligent utilization of multiple network interfaces can address the problem of cellular traffic congestion and it can also increase the frequency resources of cellular networks. Cooperative content distribution (CCD) is one such technique that can be performed by using multiple wireless interfaces. In CCD, a device receives content from a base station on its cellular interface and distributes it to other devices in its vicinity through another wireless interface such as WiFi. However, due to the broadcast nature of the secondary links such as WiFi, even a single bad link can serve as a bottleneck in terms of the CCD performance. To address this problem, in this paper, we propose a device selection method for CCD that takes into account both the primary (cellular) and secondary link (WiFi/short-range) network interfaces. The proposed method incurs little overhead as it utilizes information such as acknowledgement of data packets, that already exists in the network. We evaluate and compare (with the other content delivery methods) the performance of the proposed method in terms of: number of carriers utilized by a cellular base station (BS); average bits-per-Joule performance; and the average time required to deliver a content file. Moreover, we also take into account the impact of the presence of independent competing/interfering links (such as competing users in the unlicensed band) on the performance of the proposed method.

No abstract is provided for this article.

Polar codes have been gaining a lot of interest due to it being the first coding scheme to provably achieve the symmetric capacity of a binary memoryless channel with an explicit construction. However, the main drawback of polar codes is the low throughput of its successive cancellation (SC) decoding. Simplified SC decoding algorithms of polar codes can be used to reduce the latency of the polar decode by faster processing of specific sub-codes in the polar code. By combining simplified SC with a list decoding technique, such as SC list (SCL) decoding, polar codes can cater to the two conflicting requirements of high reliability and low latency in ultra-reliable low-latency (URLLC) communication systems. Simplified SC algorithm recognizes some special nodes in the SC decoding tree, corresponding to the specific subcodes in the polar code construction, and efficiently prunes the SC decoding tree, without traversing the sub-trees and computing log-likelihood ratios (LLRs) for each child node. However, this decoding process still suffers from the latency associated with the serial nature of SC decoding. We propose some new algorithms to process new types of node patterns that appear within multiple levels of pruned sub-trees and it enables to process of certain nodes in parallel. In short blocklength polar codes, our proposed algorithm can achieve up to $13\%$ latency reduction from fast-simplified SC \cite{Sarkis2013} without any performance degradation. Furthermore, it can achieve up to $27\%$ latency reduction if small error-correcting performance degradation is allowed.

We study the problem of joint load balancing and interference mitigation in heterogeneous networks (HetNets) in which massive multiple-input multiple-output (MIMO) macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs) are assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of small cells density, number of base station antennas, and transmit power levels at low and high frequency bands. We further provide insights into the performance analysis and convergence of the proposed framework. The numerical results show that the proposed user association algorithm outperforms other baselines. Interestingly, we find that even at lower frequency band the performance of open access small cell is close to that of closed access at some operating points, the open access full- duplex small cell still yields higher gain as compared to the closed access at higher frequency bands. With increasing the small cell density or the wireless backhaul quality, the open access full- duplex small cells outperform and achieve a 5.6x gain in terms of cell-edge performance as compared to the closed access ones in ultra-dense networks with 350 small cell base stations per km2 .