This paper considers a broadcast scenario where a base station seeks to transmit a common content to a group of users. Due to impairments inherent to broadcast channels, transmitted content may be partially received by the users, thereby requiring retransmissions to recover missing parts of the content. The retransmission process can be optimized by leveraging instantly decodable network coding (IDNC) with the help of disjoint clusters of combinations of single-hop fully connected and/or multi-hop partially connected device-to-device (D2D) network configurations. This paper aims to find an efficient encoding strategy for the broadcasted packets that minimizes the packet completion time problem, defined as the number of transmission slots necessary to satisfy the demand of all users. To this end, we design an efficient mechanism, LoCoNCDC, which enables us to find feasible codes that can be transmitted by the base station via cellular links. Moreover, the proposed LoCoNCDC permits us to identify the set of transmitting D2D users along with the corresponding IDNC packets that the transmitting users can broadcast through D2D links. Furthermore, we theoretically characterize the performance bound of the proposed approach. Finally, the efficiency of the proposed LoCoNCDC is validated through numerical results which demonstrate substantial performance gain compared to existing schemes.

Cell-Free (CF) massive multiple-input multiple-output (MIMO) system is a distributed antenna system, wherein a large number of access points wish to simultaneously communicate with a relatively small number of users. Similar to co-located massive MIMO system, pilot contamination and multi-user interference are two major impediments to CF massive MIMO network performance improvement. One can mitigate the detrimental effect of pilot contamination and multi-user interference through judicious resource allocation. In this work, we formulate and investigate the problem of frequency assignment for a CF massive MIMO. The formulated optimization problem is proven to be generally NP-hard. Local optima can be found by approaches such as the Lagrangian method that however, has considerably slow rates of convergence. To circumvent this issue, we propose an alternative solution being of two-folds. Firstly, we reformulate the problem as a grouping strategy which enables to attenuate the effect of intra-group multi-user interference. In the second fold, frequencies are assigned in a non-overlapping fashion to each scheduled group to palliate the effect of inter-group interference and pilot contamination. To further improve the performance of the proposed approach, power coefficients are allocated to the users via a sequential convex approximation (SCA)-based framework. The effectiveness of the proposed algorithms is then verified through extensive numerical simulations which demonstrate a non-negligible improvement in the performance of the studied scenario.

In this paper, we study the problem of transmittinga common content to a number of cellular users with the help of device-to-device (D2D) links via instantly decodable network coding (IDNC). In particular, a common content that is broadcast by a base station may be partially received by cellular users due to packet erasures over cellular links. D2D links among cellular users, which may be intermittently available due to mobility and link imperfections, are used to recover the missing content. In this setup, we design an IDNC mechanism to minimize packet completion time, defined as the amount of time it takes for the common content to be received by all cellular users. We develop an optimal packet completion time strategy by constructing a two-layer IDNC conflict graph where the higher layer represents all feasible network coded packets that can be transmitted over cellular links, while the lower layer consists of network coded packets (as well as corresponding cellular users that can generate and transmit these packets) that can be transmitted via D2D links. We show that finding the optimal IDNC packets to minimize the packet completion time problem is equivalent to finding the maximum independent set of the two-layer IDNC conflict graph. We also design a sub-optimal IDNC mechanism, aIDNC, that efficiently finds IDNC packets to be transmitted over cellular and D2D links. Finally, the efficiency of our proposed approach is verified through numerical experiments.

In this article, we address the problem of long-term resource allocation for a 5G and beyond dense wireless network. Of particular interest within the considered framework are machine-type communications (MTCs). To establish an optimal operation for an energy-efficient MTC system, the power allocation policy should be designed by taking into consideration both channel and queue states of the devices. This problem formulation belongs to the category of complex stochastic optimization that can be recast as a Markov decision process (MDP) over high-dimensional state space. We circumvent the hurdle inherent to state space of a huge dimension by resorting to mean-field approximation on the MDP. More specifically, we demonstrate that the formulated MDP converges to a deterministic control problem provided that the number of devices approaches infinity. We develop a low-complexity power allocation scheme to efficiently solve the original high-dimensional state space stochastic optimization problem. Our proposed power allocation policy is drawn from the solution of the Hamilton-Jacobi-Bellman equation. It can be understood as a threshold-based policy and can be implemented in a decentralized manner that makes it very appealing for high scale networks. Finally, the simulations analyses are provided to establish the effectiveness of our proposed framework.

Cell-edge caching emerges as an appealing approach to alleviate traffic load and to reduce transmission latency for future cellular networks in general and small cell networks (SCNs), in particular. In this paper, we investigate the design of a proactive caching policy that takes into account both time-correlation of user requests and the time duration users spent for a requested content; two important aspects in applications such as Video-on-Demand. The proposed caching policy is formulated as a combinatorial optimization problem. The inherently high computational complexity incurring by solving a combinatorial optimization problem is circumvented by leveraging the concept of semidefinite relaxation (SDR) method. In addition, we design a randomized procedure to efficiently find a rank one solution. Furthermore, extensive numerical results are provided in order to verify the validity of the theoretical findings and demonstrate the effectiveness of the proposed scheme.

We consider the downlink resource allocation for a multi-carrier-based asynchronous cognitive radio network with statistical knowledge of the channel state information of both primary and secondary systems available at the secondary base station. The problem of secondary utility maximization is investigated under primary and secondary user outage constraints and total power constraint. Analyses are provided for cases where both primary and secondary systems are utilizing either orthogonal frequency-division multiplexing or filter-bank based multi-carrier modulations. The highly complex yet challenging and non-convex joint power-subcarrier allocation problem is tackled by leveraging the principle of alternating optimization techniques. A per-carrier outage-based solution is provided for the subcarrier allocation stage. In the second stage, we introduce an upper bound to the outage constraints to ensure tractability. We propose an efficient convex approximation method to obtain near-optimal solutions of the centralized power control problem. Furthermore, to reduce computational complexity, a distributed implementation based on dual decomposition method is designed. Extensive simulation results are further provided to corroborate the validity of the theoretical findings and demonstrate the effectiveness of the proposed algorithms.

In this paper, we address the problem of energy-efficiency for group communications over 5G networks. We consider multicast broadcast multimedia services (MBMS) where Base Stations (BS) cooperate to convey a common information to multiple users. The problem is formulated as the global energy-efficiency (GEE) maximization under power constraints. We invoke the principle of sequential convex approximation to tackle the original non-convex problem by providing a conservative approximation. Closed-form solutions are derived and a centralized scheme is designed to find stationary solutions to the original non-convex problem. A distributed implementation with limited communication overhead among the BSs is then proposed. These theoretical findings are further validated through numerical results.

In this paper, we address the problem of minimizing channel outage probability for multi-cell multicast networks. Our main objective is to guarantee fairness and to design efficient transmit beamforming vector for physical layer multicasting. The challenging NP-hard problem is tackled by leveraging the principle of sequential parametric convex approximation. More specifically, the original nonconvex problem is replaced by a sequence of successively refined approximated problems. We derive the closed-form solution for each approximated problem. In addition to that, we design a centralized algorithm and demonstrate that it can be implemented in a distributed fashion. The proposed algorithm is guaranteed to converge to a stationary solution of the original problem. Numerical results are then provided to demonstrate the effectiveness of our theoretical findings.

This paper addresses the problem of single-cell point-to-multipoint communications. We consider a base station that needs to transmit a common information to a group of users. We propose a layered Wyner-Ziv coding (WZC) scheme based on nested lattice quantization. In our scheme, we use the base layer generated by a standard coder as the decoders side information. Then, WZC is performed for quality enhancement of the transmitted message. With the proposed layered coding, multiple refinement messages will be generated. The layered messages can be decoded each by a subgroup of users based on their channel conditions. We optimize the scheme parameters in order to maximize the end-to-end multicast rate and minimize the reconstruction distortions experienced at each subgroup of users. Numerical results show that our scheme performs well when compared with the basic multicasting scheme.

This paper studies resource allocation for a multi-carrier based cognitive radio network under the assumption of statistical channel state information (CSI). To circumvent the inherent high computational complexity investigating the joint power-rate-subcarrier of the outage-constrained sum rate maximization problem, we adopt a sub-optimal strategy by solving independently the subcarrier and power-rate problem. Firstly, we propose a heuristic subcarrier allocation paradigm by utilizing an outage-based metric. Secondly, we conservatively approximate the intractable non-convex powerrate control problem and propose a sequential-based algorithm to efficiently obtain a solution to the problem. The proposed algorithm has been shown to converge to solutions that are stationary points of the original power-rate problem. Extensive simulation results are provided to demonstrate the efficiency of our proposed algorithm.

This paper addresses the problem of energy-efficiency (EE) within an underlay cognitive radio (CR) network where primary users (PUs) and secondary users (SUs) utilize two types of multicarrier modulation: orthogonal frequency-division multiplexing (OFDM) and filter bank based multicarrier (FBMC). The problem of non-cooperative SU downlink energy-efficiency is investigated using a game theoretic approach. We reformulate the EE optimization problem as a non-cooperative power allocation game (NPAG). A distributed power control algorithm is proposed to find the optimal power allocation strategy for each secondary base station on each subcarrier. The proposed non-cooperative game is demonstrated to reach a Nash-equilibrium (NE) point. Simulation analyses are then provided in order to validate the efficiency of our proposed distributed algorithm and also to highlight the advantages of using FBMC as a modulation technique if compared to OFDM.

For wireless amplify-and-forward (AF) relay networks, this work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework. It is proved that under two sufficient conditions: 1) the objective function of the random search algorithm is continuous and all its local maxima are global maxima in the considered feasible set, and 2) the origin is an interior point within the support of the probability measure for the random perturbation, the corresponding adaptive distributed beamforming schemes converge almost surely. While the second sufficient condition can be controlled by system designer and satisfied with relative ease, the first sufficient condition initially seems strict. Surprisingly, further analysis on the signal-to-noise ratio (SNR) functions in AF relay networks with individual and total power constraints demonstrates that, in both scenarios, local maxima are global maxima and hence, the first sufficient condition is satisfied. Finally, the proposed framework was extended to analyze adaptive distributed beamforming schemes in an asynchronous setting, and simulation results were provided to further validate our analysis.

This paper investigates the problem of adaptive distributed beamforming for wireless sensor/relay networks. Existing schemes seek to adaptively achieve distributed phase alignment for beamforming via the help of an error-free, one-bit feedback link from the receiver to all distributed transmitters. Under such setting, we proposed a Deterministic Bisection Search Algorithm (DBSA), designed to achieve distributed phase alignment efficiently in a greedy fashion. Extensive simulations demonstrated significant improvement in the convergence time of the proposed DBSA compared with existing schemes. For wireless sensor/relay networks, inherent issues of system uncertainty such as random node addition or removal often occur due to power depletion or requests from new nodes to join the network. To resolve these issues, DBSA was further extended and numerical experiments have shown that the extended version is indeed robust against such system uncertainty.