Downlink resource allocation under channel uncertainties for device-to-device communication

In this paper, we study a device to device (D2D) communications underlaying cellular network where D2D pairs can reuse a cellular user (CU) resource block (RB) simultaneously, to share multimedia files. It is well known that the delay plays an important role in real-time services. Here, to ensure the fairness between D2D pairs while satisfying the quality of service (QoS) requirements, we maximize the minimum utility function of D2D users under delay and interference restrictions. The sigmoid function is used as the utility function in the optimization problem. We show that this optimization problem is NP-hard and thus, we introduce an effective iterative method to solve it. Simulation results reveal significant improvement in comparison to other power allocation schemes and demonstrate that the proposed scheme can effectively guarantee the fairness between D2D pairs under QoS conditions of CU and D2D pairs. Moreover, simulation results also indicate that the convergence rate of our proposed algorithm is quite fast.

In underlay device-to-device (D2D) communication, a D2D pair reuses the cellular spectrum and creates interference to regular cellular users. Optimal operation requires joint consideration for the achieved D2D rate and the added interference to cellular users. Most existing work on D2D rate maximization concerns only the simplified scenario where the D2D pair has access to a single channel or resource block. In this work, we present an optimization solution to allocate the D2D transmission power over multiple channels, to maximize the sum rate between D2D and cellular users, under a sum-power constraint on the D2D transmitter and minimum SINR guarantees at each RB for all cellular users. The proposed optimization is applicable to both uplink and downlink cellular spectrum sharing. Our simulation studies further shed light into how the maximum sum rate is impacted by the available D2D power and the SINR guarantees.

In this study, the authors focus on segment characteristic analysis and joint optimisation of modulation and coding scheme (MCS) selection, resource block (RB) assignment and the block error ratio (BLER) determination and segment adaptation scheme to satisfy dynamic adaptive streaming over HTTP (DASH) clients over wireless networks. First, the authors define a utility function as the continuous playback time that the packets scheduled with the allocated resource can support. Then, the authors formulate the MCS selection, RB assignment and BLER determination into a mathematical model. The relationship among the above three factors can be explored instead of performing MCS selection and RB assignment with the fixed BLER. By decomposing the original problem into some sub‐problems, the authors can get a solution to the original problem with low complexity. At the client level, the authors develop an adaptive segment request strategy based on the playback information, the segments' characteristics and the estimated transmission rate. To decrease the influence of the inaccurate estimations, an adaptive guard time interval based on the real playback information and transmission information of the previous segments is introduced. Simulation results show that the proposed algorithm can efficiently improve playback continuity and video quality over the existing algorithms.

We propose a decentralized strategy for downlink interference management in overlaid MIMO femtocell-macrocell deployments operating in the same band under OFDMA access. The resource allocation, consisting of MIMO precoders at the macro base station (MBS) and femto access points (FAPs) and possibly the resource block (RB) assignment, is designed to maximize the Weighted Sum Rate (WSR) of the system. Being the overall problem non-convex, we propose to solve it in a decentralized way where each transmitter designs its resource allocation using interference prices exchanged with other transmitters (FAPs and MBS) by exploiting the wired backhaul connection. The problem includes a rate restriction per FAP that accounts for the limited bitrate provided by the Internet Service Provider (ISP) wired backhaul. Two efficient algorithms are provided for: 1) optimizing MIMO precoders and 2) optimizing both MIMO precoders and RB assignment. Numerical results compare decentralized strategies based on pricing exchange and on pure competition in dense FAP deployments.

The design problem associated with robust downlink beamforming in multicast, multigroup, multicell wireless systems is addressed. The channel state information (CSI) of users is assumed to be imperfect and the uncertainty of CSI is modelled using the Frobenius norm. The objective is to optimise the signal-to-interference-plus-noise ratio over all users with a constraint on the maximum total transmitted power. This was achieved through a robust solution using the successive convex approximation (SCA) method. The beamforming problem is treated as a bi-convex problem, which is solved using the iterate-alternative convex technique. Here, the CSI uncertainty is addressed using a convex package through the non-monotone spectral projected gradient method and the beamforming vector is extracted using the SCA method. Also, the authors offer the required condition to extract the beamform vector using the SCA method through a suboptimal solution that always addressed before using different beamforming methods. Their simulation results examine all proposed system parameters in order to show convergence and feasibility of the solution. They also compare the solution with a suboptimal solution and the quality of service method for imperfect CSI in downlink beamforming. Numerical results show that the robust solution achieves the best power efficiency for practical solutions.

We study a beamformed Alamouti coding scheme for a multigroup multicast downlink system. In this letter, we take into account imperfect channel state information (CSI) at the base station, where the CSI uncertainty region is formed by the intersection of multiple ellipsoids. This unified CSI uncertainty model leads to a challenging robust rank-two multicast beamforming problem. We first develop a conservative approximation method to handle the intractable constraints in the formulated robust beamforming problem under the considered CSI uncertainty region. Then, we apply the semidefinite relaxation (SDR) technique to approximately solve this problem. Our rank-profile analysis shows that the SDR is tight when there are at most two users in each multicast group and the CSI uncertainty region is not too large. Finally, we carry out simulations to validate the effectiveness of the proposed conservative approximation method. The simulation results coincide with the rank-profile analysis.

In this study, the authors consider the power optimisation based on the MOS (mean of score) models of different services over multi-user femtocell systems. They first formulate a mathematic model to minimise the total power consumption, subject to the MOS requirements and the network resource constraint. To solve the formulated problem, they first propose an optimal power allocation (OPA) scheme, in which the optimal power allocation and RB (resource block) assignment can be obtained by employing the Lagrange dual decomposition method. However, iterative update of the Lagrange variables leads to high complexity. To deal with this, they further propose a utility-based suboptimal power optimisation algorithm (SPA). The utility of assigning each RB to each user is defined as the power consumption difference caused by adding the RB to the set of the RBs already assigned to the user. To calculate the utility, they develop a low-complexity optimal power allocation scheme. Based on the calculated utility, each RB is assigned to the user with the maximum utility. Experimental results demonstrate that OPA can indeed acquire an upper bound of the system performance. SPA can improve the MOS of the users with acceptable complexity compared with other existing algorithms.

The most precious resource for the mobile network operators (MNOs) is the radio spectrum, cardinal for meeting the quality of service (QoS) requirements of the users. An integral technology of the budding fifth generation (5G) wireless communication networks (WCNs) is device-to-device (D2D) communication. To make use of the gains offered by D2D communication, optimal resource sharing with the cellular users in the network is necessitated. This paper proposes an adaptive resource block (RB) allocation scheme, using Hidden Markov Model (HMM), to assure adequate resource availability to each demanding D2D pair in the network. The adaptive allocation of resources improves the system throughput and energy efficiency (EE), meeting the QoS demands of the D2D pairs and fostering green communication. Since RB sharing is between pairs and cellular users, interference rises. The improvement in performance is achieved by sectoring the cellular coverage region, which diminishes the interference levels. The potency of the proposed adaptive scheme has been verified through simulations.

A novel algorithm is proposed for joint minimum mean-squared error based optimal secondary user transceiver design and user/antenna selection for multiple input multiple-output orthogonal frequency division multiplexing cognitive radio networks. Closed form expressions are developed for the optimal transceiver considering the availability of both full as well as partial channel state information (CSI) of the primary user at the secondary users. Further, optimal transceiver design is also considered for a realistic scenario with CSI uncertainty. In this regard, a low complexity algorithm is presented for joint secondary user and antenna selection towards sum-MSE minimization in cognitive radio networks. Simulation results demonstrate the improved performance of the proposed transceiver design and user/antenna selection algorithm in terms of the MSE performance at the receiver.

In this paper, we propose a system model for cellular vehicle-to-everything (C-V2X) networks mode-3, with two user groups: cellular users and vehicular users. Cellular users communicate with each other through the base station (BS). Vehicular users communicate through direct vehicular links while sharing the spectrum of an orthogonal frequency division multiplexing (OFDM) system with cellular users. In particular, we focus on the underlay approach, in which vehicular users can transmit at the same time in a sub-carrier that is being used by a cellular user. Assuming a limited number of sub-carriers, the number of allowed vehicular links is limited by the quality of service (QoS) requirement for cellular users because of the interference from vehicular links. Our goal is to maximize the number of vehicular links while satisfying the QoS for cellular users. To achieve this goal, we state the optimization problem of resource allocation in C-V2X networks mode-3, assuming high mobility of vehicular users, which results in Doppler effect for them. We also assume that the global channel state information (CSI) of mobile links is not available for the BS. We propose a novel scheme for the BS to jointly allocate resources, such as power and sub-carriers, to cellular and vehicular users, while minimizing the number of non-allowed vehicular links, considering high data rate requirements for cellular users and high reliability requirements for vehicular users. Using numerical results, we obtain the maximum number of vehicular links that can be established for a given available bandwidth and cellular user distribution.

This letter investigates the bit error rate (BER) performance of orthogonal frequency division multiplexing index modulation, employing the maximal ratio combining-based low-complexity greedy detector (MRC-GD) and the PSK modulation. For performance analysis, we derive tight expressions for both index error probability (IEP) and BER, taking into account channel state information (CSI) uncertainty. This provides insight into various impacts of CSI uncertainty on the diversity gain and error floor of the IEP and the BER, respectively. We clearly show that under imperfect CSI, the MRC-aided GD can perform like the MRC-maximum likelihood detector, at lower complexity. Finally, simulation results are presented to verify the accuracy of derived expressions and the theoretical analysis.

This paper studies the impact of channel error on the achievable rate of symmetrical K-user multiple-input multiple-output linear interference alignment (IA) networks. The upper and lower bounds of the achievable sum rate are derived analytically with the assumption of orthonormal transmit precoders and receive filters designed from imperfect channel state information (CSI) over both the uncorrelated and correlated channels. For uncorrelated channels, quite tight lower and upper bounds are obtained. The impact of channel error on the degrees of freedom (DoF) and the DoF persistence conditions are also investigated. Results show that the DoF of IA networks persists only if the channel error decreases in an order higher than the signal-to-noise ratio. For correlated channel, the lower and upper bounds for one realization of IA are derived. The derived upper bound can be used to characterize the achievable rate approximately. Simulation results indicate that the achievable rate of IA network is influenced significantly by CSI uncertainty. The obtained analytical bounds provide an intuitive way to show the impact of channel error on the achievable rate and thus can help practical systems deign.

Recently, device-to-device (D2D) communication and full duplex communication are both considered key technologies for 5G networks. In this paper, two novel cooperative modes are developed for full duplex D2D communication underlaying cellular networks: the network MU-MIMO based mode (N-mode) and the sequential forwarding mode (S-mode). In the N-mode, two D2D users work as network MIMO to forward the data to cellular users and thus leverage the channel diversity. In the S-mode, the spatial distribution of D2D users and cellular users is explored to improve the transmission rate. Based on these modes, two D2D users share the downlink resources with two nearby cellular users simultaneously to achieve both proximity gain and reuse gain. Moreover, these modes are well suited for edge cellular users. To optimize the performance of the two modes, optimal power allocation is conducted by considering the influence of residual self-interference at full duplex radios and the requirements of the minimum transmission rate for both cellular users and D2D users. Simulation results show that, compared with the dedicated mode, the sum rate of cooperative modes in the perfect self-interference cancellation case can achieve 32% overall improvement and about 70% improvement in the scenario with non-edge D2D users and edge cellular users. With residual self-interference at a full duplex radio, the overall performance improvement is reduced to 15%. However, the cooperative modes can still achieve 40% improvement in the scenario with non-edge D2D users and edge cellular users.

We propose a novel restriction and relaxation (RAR) method for robust relay beamforming design in cognitive two-way relay networks with imperfect channel state information (CSI). Due to the uncertainty in CSI, the self-interference cannot be completely canceled. Hence, both channel uncertainties and residual self-interference should be considered. The proposed RAR method aims to minimize the transmit power of relay nodes while guaranteeing the worst-case signal-to-interference plus noise ratio (SINR) of secondary users (SUs) as well as satisfying the interference temperature (IT) constraints. Specifically, in the restriction step of RAR, infinite number of complicated constraints are reformulated into a finite number of linear matrix inequalities (LMIs). In the relaxation step of RAR, the nonconvex robust design problem is transformed into a convex semidefinite program (SDP), which can be efficiently solved by interior-point methods. Simulation results verify the effectiveness and robustness of the proposed method.

A Gaussian multiple-input single-output (MISO) secrecy channel is considered, where a transmitter is equipped with multiple antennas, and an intended receiver and an eavesdropper each equipped with a single antenna. This paper studies the robust beamforming design in the presence of uncertainty of channel state information (CSI) on the eavesdropper channel. Under the assumption that all possible states of the eavesdropper channel are in a given set, the problem of optimizing the transmit covariance for the worst-case secrecy capacity which is the maximum transmission rate at which the eavesdropper is unable to decode under any channel realization in the set, is cast as a max-min optimization problem. We propose to transfer the max-min problem into a convex optimization problem that can be solved efficiently. The optimal secrecy rate and transmit covariance are expressed in the form of the generalized eigenvalue and eigenvector, the same form as the conventional case of the perfect eavesdropper's CSI, which indicates our results is the generalization of the conventional case.