Publications

Partially connected hybrid beamforming (HBF) is a promising approach to alleviate the implementation of large scale millimeter-wave multiple-input multiple-output (MIMO) systems. In this paper, we develop rate maximizing algorithms for the full array-and subarray-based processing strategies of partially connected HBF. We formulate the rate maximization problem as a weighted mean square error minimization problem and use alternating optimization to tackle it. Numerical results show that partially connected HBF provides a good balance between hardware complexity and performance in comparison to optimal fully digital and analog beamforming. Moreover, the simpler subarray-based HBF algorithm achieves comparable performance to that of the full array-based approach in medium and high SNRs. The rate maximizing results serve as upper bounds for lower complexity heuristic methods.

This paper investigates a hybrid network configuration in which full-duplex base stations serve half-duplex users on both Uplink and Downlink simultaneously. Users are modeled as a homogeneous Poisson point process while the channel is modeled as a composite fading with correlated Log-normal shadowing and Nakagami-m fading. We characterize the signal-to-interference-ratio at an user of interest, and then evaluate how the network performs in terms of outage probability. We account for the cross-correlation between the user of interest and a random co-site interferer within range. Thus, we provide a valuable insight on on how the hybrid network performs under the assumption of correlated shadowing. We show that when the aforesaid correlation is low, the user of interest can achieve higher data rate at expense of high outage; however, if the distance to the serving base station is short and the cross correlation is high, a satisfactory data rate can be sustained at low outage.

The growing demand in indoor small cell networks has given rise to the concept of micro-operators (MOs) for local service delivery. We model and analyze a spectrum sharing system involving such MOs where a buyer MO buys multiple licensed subbands provided by the regulator. Also, all small cell base stations (SBSs) owned by a buyer MO can utilize multiple licensed subbands at the same time which are also used by other MOs. A deterministic model in which the location of the SBSs are known can lead to unwieldy problem formulation, when the number of SBSs is large. Subsequently, we adopt a stochastic geometric model of the SBS deployment instead of a deterministic model. Assuming that the locations of the SBSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability and average data rate for a typical user (UE) served by the buyer MO in a spectrum sharing environment. In order to satisfy the QoS constraint, we provide a greedy algorithm to find how many licensed subbands and which subband for the buyer MO to purchase from the regulator. We also derive the coverage probability of the buyer MO for interference the limited system.

Femtocells are envisioned to be deployed in indoor environments in order to improve both radio coverage and spectrum efficiency. This paper focuses on the self-organization of indoor femtocells, which includes mechanisms of cooperation. In this context, we propose a solution to automatically form cooperating groups among severely interfered femtocells in order to avoid interference. A hybrid access policy is proposed and compared to the closed and open policies. Results show that, as stated in the Braess Paradox, pervasive cooperation may be detrimental, when available resources are highly contended. Conversely, in particular cases, a marginal selfish behavior of femtocells can be preferable.

This paper presents an overview of approaches for remote and rural area (RRA) connectivity, including Brazilian regulations for the use of television white spaces (TVWSs) and approaches taken in Finland. This effort represents the first step for developing a future mobile network that can address the Brazilian and Finnish challenges. It also presents one radio solution for connectivity in RRAs: a GNU Radio-based transceiver that explores different technologies to provide reliable and high capacity links aiming to fulfill the RRA demands. Besides, a brief review of spectrum sensing reveals opportunities to further improve the transceiver by implementing alternative sensing approaches. The set of techniques implemented with the associated low cost and flexibility to address several requirements, as well as the good performance observed in laboratory and field experiments, show that this transceiver can be a reliable solution for exploring TVWSs in RRAs and, more specifically, a promising solution for connectivity in remote areas.

Wireless energy transfer (WET) is a green enabler of low-power Internet of Things (IoT). Therein, traditional optimization schemes relying on full channel state information (CSI) are often too costly to implement due to excessive energy consumption and high processing complexity. This letter proposes a simple, yet effective, energy beamforming scheme that allows a multi-antenna power beacon (PB) to fairly power a set of IoT devices by only relying on the first-order statistics of the channels. In addition to low complexity, the proposed scheme performs favorably as compared to benchmarking schemes and its performance improves as the number of PB's antennas increases. Finally, it is shown that further performance improvement can be achieved through proper angular rotations of the PB.

In this paper, we introduce a new load balancing user association scheme for millimeter wave (mmWave) cellular networks in which intelligent reflecting surface (IRS) is applied in the cellular network to improve the coverage region of each cell and mitigate mmWave vulnerability to non-line of sight (N-LoS) paths. The user association scheme improves network performance significantly by adjusting the interference according to the association. We study the IRS-assisted mmWave cellular network where one IRS is deployed to assist in the communication from the base station (BS) to mobile users (MUs) in each cell. We balance BS loads and maximize a network utility by optimizing the user association with a matching game. Simulation results show that the proposed scheme significantly improves the throughput compared to conventional user association techniques.

Data aggregation is a promising approach to enable massive machine-type communication (mMTC). This paper focuses on the aggregation phase where a massive number of machine-type devices (MTDs) transmit to aggregators. By using non-orthogonal multiple access (NOMA) principles, we allow several MTDs to share the same orthogonal channel in our proposed hybrid access scheme. We develop an analytical framework based on stochastic geometry to investigate the system performance in terms of average success probability and average number of simultaneously served MTDs, under imperfect successive interference cancellation (SIC) at the aggregators, for two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). We identify the power constraints on the MTDs sharing the same channel to attain a fair coexistence with purely orthogonal multiple access (OMA) setups, then power control coefficients are found so that these MTDs perform with similar reliability. We show that under high access demand, the hybrid scheme with CRS outperforms the OMA setup by simultaneously serving more MTDs with reduced power consumption.

No abstract is provided for this article.

Effective resource allocation is a crucial requirement to achieve the stringent performance targets of ultra-reliable low-latency communication (URLLC) services. Predicting future interference and utilizing it to design efficient interference management algorithms is one way to allocate resources for URLLC services effectively. This paper proposes an empirical mode decomposition (EMD) based hybrid prediction method to predict the interference and allocate resources for downlink based on the prediction results. EMD is used to decompose the past interference values faced by the user equipment. Long short-term memory and auto-regressive integrated moving average methods are used to predict the decomposed components. The final predicted interference value is reconstructed using individual predicted values of decomposed components. It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by $20 - 25\%$. The proposed resource allocation algorithm utilizing the EMD-based interference prediction was found to meet near-optimal allocation of resources and correspondingly results in $2-3$ orders of magnitude lower outage compared to state-of-the-art baseline prediction algorithm-based resource allocation.

Data aggregation is a promising approach to enable massive machine-type communication. This paper focuses on the aggregation phase where a massive number of machine-type devices (MTDs) transmit to aggregators. By using non-orthogonal multiple access (NOMA) principles, we allow several MTDs to share the same orthogonal channel in our proposed hybrid access scheme. We develop an analytical framework based on stochastic geometry to investigate the system performance in terms of average success probability and average number of simultaneously served MTDs, under imperfect successive interference cancellation (SIC) at the aggregators, for two scheduling schemes: random resource scheduling and channel-aware resource scheduling (CRS). We identify the power constraints on the MTDs sharing the same channel to attain a fair coexistence with purely orthogonal multiple access (OMA) setups. Then, power control coefficients are found, so that these MTDs perform with similar reliability. We show that under high access demand, the hybrid scheme with CRS outperforms the OMA setup by simultaneously serving more MTDs with reduced power consumption.

To satisfy the capacity requirements of future mobile systems, under-utilized millimeter wave frequencies can be efficiently exploited by employing massive MIMO technology with highly directive beamforming.Hybrid analog-digital beamforming has been recognised as a promising approach for large-scale MIMO implementations with a reduced number of costly and power-hungry RF chains.In comparison to fully connected architecture, hybrid beamforming (HBF) with partially connected RF architecture is particularly appealing for the practical implementation due to less complex RF power division and combining networks.In this paper, we first formulate single- and multi-user rate maximization problems as weighted minimum mean square error (WMMSE) and derive solutions for hybrid beamformers using alternating optimization.The algorithms are designed for the full-array- and sub-array-based processing strategies of partially connected HBF architecture.In addition to the rate maximizing WMMSE solutions, we propose lower complexity sub-array-based zero-forcing algorithms.The performance of the proposed algorithms is evaluated in two different channel models, i.e., a simple geometric model and a realistic statistical millimeter wave model known as NYUSIM.The performance results of the WMMSE HBF algorithms are meant to reveal the potential of partially connected HBF and serve as upper bounds for lower complexity methods.Numerical results imply that properly designed partially connected HBF has the potential to provide an good compromise between hardware complexity and system performance in comparison to fully digital beamforming.

The design of distributed mechanisms for interference management is one of the key challenges in emerging wireless small cell networks whose backhaul is capacity limited and heterogeneous (wired, wireless and a mix thereof). In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed. The proposed approach enables macrocell user equipments (MUEs) to optimize their uplink performance, by exploiting the presence of neighboring small cell base stations. The problem is formulated as a noncooperative game among the MUEs that seek to optimize their delay-rate tradeoff, given the conditions of both the radio access network and the -- possibly heterogeneous -- backhaul. To solve this game, a novel, distributed learning algorithm is proposed using which the MUEs autonomously choose their optimal uplink transmission strategies, given a limited amount of available information. The convergence of the proposed algorithm is shown and its properties are studied. Simulation results show that, under various types of backhauls, the proposed approach yields significant performance gains, in terms of both average throughput and delay for the MUEs, when compared to existing benchmark algorithms.

No abstract is provided for this article.

Self-energyrecycling (sER), which allows transmit energy reutilization, has emerged as a viable option for improving the energy efficiency (EE) in low-power Internet of Things networks. In this article, we investigate its benefits also in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multiantenna techniques in a communication system. We analyze the tradeoffs when considering not only the energy spent on transmission but also the circuitry power consumption, thus making the analysis of much more practical interest. In addition to the well-known spectral efficiency improvements, results show that FD also outperforms HD in terms of reliability. We show that sER introduces not only benefits in EE matters but also some modifications on how to achieve maximum reliability fairness between uplink and downlink transmissions, which is the main goal in this article. In order to achieve this objective, we propose the use of a dynamic FD scheme where the small base station determines the optimal allocation of antennas for transmission and reception. We show the significant improvement gains of this strategy for the system outage probability when compared to the simple HD and FD schemes.