Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are gaining interest recently due to their improved performance and flexibility; particularly for communication scenarios in which high-performing structured code designs do not exist. Communication in the presence of feedback is one such communication scenario, and practical code design for feedback channels has remained an open challenge in coding theory for many decades. Recently, DNN-based designs have shown impressive results in exploiting feedback. In particular, generalized block attention feedback (GBAF) codes, which utilizes the popular transformer architecture, achieved significant improvement in terms of the block error rate (BLER) performance. However, previous works have focused mainly on passive feedback, where the transmitter observes a noisy version of the signal at the receiver. In this work, we show that GBAF codes can also be used for channels with active feedback. We implement a pair of transformer architectures, at the transmitter and the receiver, which interact with each other sequentially, and achieve a new state-of-the-art BLER performance, especially in the low SNR regime.

We study downlink channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in time-division duplex. The users must know their effective channel gains to decode their received downlink data. Previous works have used the mean value as the estimate, motivated by channel hardening. However, this is associated with a performance loss in non-isotropic scattering environments. We propose two novel estimation methods that can be applied without downlink pilots. The first method is model-based and asymptotic arguments are utilized to identify a connection between the effective channel gain and the average received power during a coherence interval. The second method is data-driven and trains a neural network to identify a mapping between the available information and the effective channel gain. Both methods can be utilized for any channel distribution and precoding. For the model-aided method, we derive all expressions in closed form for the case when maximum ratio or zero-forcing precoding is used. We compare the proposed methods with the state-of-the-art using the normalized mean-squared error and spectral efficiency (SE). The results suggest that the two proposed methods provide better SE than the state-of-the-art when there is a low level of channel hardening, while the performance difference is relatively small with the uncorrelated channel model.

Cellular network operators have witnessed signi cant growth in data tra c in the past few decades.This growth occurs due to the increase in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services.As the available frequency bandwidth for cellular communication is limited, signi cant e orts are dedicated to improving the utilization of available spectrum and increasing the system performance with the aid of new technologies.Third-generation (3G) and fourth-generation (4G) mobile communication networks were designed to facilitate high data tra c in cellular networks in past decades.Nevertheless, there is still a requirement for new cellular network technologies to accommodate the ever-growing data tra c demand.The fth-generation (5G) is the latest generation of mobile communication systems deployed and implemented around the world.Its objective is to meet the tremendous ongoing increase in the data tra c requirements in cellular networks.Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks.Massive MIMO originated from the concept of multi-user MIMO.It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks.With Massive MIMO technology, there is a notable enhancement of both sum spectral e ciency (SE) and energy e ciency (EE) in comparison with conventional MIMO-based cellular networks.Resource allocation is an imperative factor to exploit the speci ed gains of Massive MIMO.It corresponds to e ciently allocating resources in the time, frequency, space, and power domains for cellular communication.Power control is one of the resource allocation methods of Massive MIMO networks to deliver high spectral and energy e ciency.Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric.The rst part of this thesis investigates reusing a Massive MIMO network's resources for direct communication of some speci c user pairs known as deviceto-device (D2D) underlay communication.D2D underlay can conceivably increase i

We study downlink (DL) channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in a time-division duplex. The users must know their effective channel gains to decode their received DL data signals. A common approach is to use the mean value as the estimate, motivated by channel hardening, but this is associated with a substantial performance loss in non-isotropic scattering environments. We propose two novel estimation methods. The first method is model-aided and utilizes asymptotic arguments to identify a connection between the effective channel gain and the average received power during a coherence block. The second one is a deep-learning-based approach that uses a neural network to identify a mapping between the available information and the effective channel gain. We compare the proposed methods against other benchmarks in terms of normalized mean-squared error and spectral efficiency (SE). The proposed methods provide substantial improvements, with the learning-based solution being the best of the considered estimators.

The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and furt ...

In this paper, we consider device-to-device (D2D) communication that is underlaid in a multi-cell massive multiple-input multiple-output (MIMO) system and proposes a new framework for power control and pilot allocation. In this scheme, the cellular users (CUs) in each cell get orthogonal pilots which are reused with reuse factor one across cells, while all the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the CUs with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. We provide power control algorithms to maximize the minimum spectral efficiency (SE) and to maximize the product of the signal-to-interference-plus-noise ratios in the network. Different from prior works, in our proposed power control schemes, we consider joint pilot and data transmission optimization. Finally, we provide a numerical evaluation, where we compare our proposed power control schemes with the maximum transmit power case and the case of conventional multi-cell massive MIMO without D2D communication. Based on the provided results, we conclude that our proposed scheme increases the sum SE of multi-cell massive MIMO networks.

This paper studies the transmit power optimization in a multi-cell massive multiple-input multiple-output (MIMO) system. To over-come the scalability issue of network-wide max-min fairness (NW-MMF), we propose a novel power control (PC) scheme. This scheme maximizes the geometric mean (GM) of the per-cell max-min spectral efficiency (SE). To solve this new optimization problem, we prove that it can be rewritten in a convex form and then solved using standard tools. To provide a fair comparison with the available utility functions in the literature, we solve the network-wide proportional fairness (NW-PF) PC as well. The NW-PF focuses on maximizing the sum SE, thereby ignoring fairness, but gives some extra attention to the weakest users. The simulation results highlight the benefits of our model which is balancing between NW-PF and NW-MMF.

This paper proposes a new power control and pilot allocation scheme for device-to-device (D2D) communication underlaying a multi-cell massive MIMO system. In this scheme, the cellular users in each cell get orthogonal pilots which are reused with reuse factor one across cells, while the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the cellular users with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. Then we provide a power control algorithm that maximizes the minimum spectral efficiency (SE) of the users in the network. Finally, we provide a numerical evaluation where we compare our proposed power control algorithm with the maximum transmit power case and the case of conventional multi-cell massive MIMO without D2D communication. Based on the provided results, we conclude that our proposed scheme increases the sum spectral efficiency of multi-cell massive MIMO networks.

In order to minimize electric grid power consumption, energy harvesting from ambient RF sources is considered as a promising technique for wireless charging of low-power devices. To illustrate the design considerations of RF-based ambient energy harvesting networks, this article first points out the primary challenges of implementing and operating such networks, including non-deterministic energy arrival patterns, energy harvesting mode selection, energy-aware cooperation among base stations, and so on. A brief overview of recent advances and a summary of their shortcomings are then provided to highlight existing research gaps and possible future research directions. To this end, we investigate the feasibility of implementing RF-based ambient energy harvesting in ultra-dense small cell networks and examine the related trade-offs in terms of the energy efficiency and signal-to-interference-plus-noise ratio outage probability of a typical user in the downlink. Numerical results demonstrate the significance of deploying a mixture of on-grid small base stations (powered by electric grid) and off-grid small base stations (powered by energy harvesting), and optimizing their corresponding proportions as a function of the intensity of active small base stations in the network.

In this paper, a novel algorithm is proposed for joint power loading and resource allocation in network-assisted device-to-device (D2D) underlay communications. The non-convex problem of minimizing sum transmit power of system while satisfying per user rate constraints is solved via successive convex approximation (SCA). A single cell system with a number of D2D pairs is considered, where the communication links are allocated to operate in either cellular, orthogonal or direct D2D mode depending on the system setting. The robustness of the algorithm is guaranteed by utilizing augmented Lagrangian relaxation of the rate constraints with additional quadratic penalty terms. Numerical results indicate that the proposed algorithm is efficiently allocating the optimal power and resources for the D2D pairs. Furthermore, the sum power performance is improved when comparing to the scenario where all links are allocated solely either to the D2D mode or cellular transmissions modes.

In this paper, we consider a single-cell system where the same radio resources are simultaneously used by a cellular user and a pair of device-to-device (D2D) terminals. The optimization objective is to minimize the sum transmission power of the system while satisfying the user specific minimum rate constraints. We propose joint power control and beamforming algorithms to solve the power minimization problem optimally both in uplink and downlink. In the uplink, the optimal transmit powers for the cellular and D2D users are obtained via fixed-point iterations, whereas the linear minimum mean squared error receiver is used for optimal reception at the base station (BS). In the downlink, the problem is equivalently reformulated as a second-order cone program (SOCP). As a result, the optimal transmit beamformer for the cellular user and the optimal transmit power for the D2D user can be efficiently computed via standard SOCP solvers. Simulation results demonstrate that the sum power performance can be significantly improved as compared with the conventional cellular system. Results also illustrate that the power consumption of the network is highly affected by the locations of the cellular and D2D users, and whether the resources are shared in the uplink or downlink.

Since the integration of data services into cellular communications, cellular operators are struggling to harness the overwhelming data traffic on their networks. Underlay Device-to-Device (D2D) communication is a new and promising paradigm which allows proximate mobile users to have direct communication over the cellular spectrum that may be reused by other cellular users in the same cell. This new paradigm is proposed to assist the cellular operators to deal with the booming demand of mobile users. Recent studies have shown that underlay D2D communication significantly increases the cellular network capacity, and enables cellular operators to support rich multimedia services. However, reusing cellular resources for both D2D and cellular communication introduces interference issues. In such systems, interference management is of utmost importance because improper interference coordination may lead to a self-destructive network. Power control and beamforming appears to be viable techniques for interference management which can also be used to enhance the energy efficiency of the system. Network coordinated sum power optimization schemes for D2D communications underlaying uplink and downlink cellular spectrum is considered in this thesis. In particular, the system optimization target is to minimize the sum transmission power while guaranteeing the user specific rate constraints. Novel algorithms are proposed to solve the power minimization problem optimally. For the uplink, the problem is solved using joint transmit power control and receive beamforming algorithm. The downlink problem is reformulated as a second-order cone program (SOCP), and thus, it can be solved efficiently via standard SOCP solvers. Moreover, a decentralized algorithm is proposed that reduces the amount of control information exchange in comparison to the centralized approach. The performance of the proposed algorithms is compared with the conventional cellular scheme. Simulation results demonstrate that the proposed underlay D2D communication approach is capable of achieving significant performance gains over the conventional cellular scheme. Results also illustrate that the power consumption of the system is highly affected by the location of the interfering cellular user and whether the resources are shared in uplink or downlink. Therefore, four different resource sharing areas are defined for D2D communications. These areas specify the type of resources (i.e., downlink and uplink) suitable for D2D communication.

This paper studies possibility of using full-duplex (FD) radios in underlay device-to-device communication (D2D) in cellular networks. We consider a cellular system with one D2D pair and one cellular user. Cellular user is sharing the radio resources with D2D link which is equipped with full-duplex radios. The problem of sum-power minimization of cellular system and D2D link both in uplink and downlink period is considered. Considering the interference caused because of exploiting the same radio resources, for uplink period, we use fixed point iterations to solve the optimization problem to calculate transmit powers of users. To design the optimal receiver in the base station, linear minimum mean squared error method is used. In the downlink, to calculate optimal transmit precoder at base station and optimal D2D transmit powers, the optimization problem is formulated as a second order cone problem (SOCP) and solved using CVX in Matlab. Since the available full-duplex radios are not able to cancel the self-interference completely, residual of self-interference is considered in D2D receivers. Performance of the full-duplex D2D with different amounts of self-interference cancelation is compared to that of half-duplex D2D. Results show that full-duplex radios with 110 dB self-interferece cancelation can provide double the throughput for D2D compared to half-duplex radios.