The joint power allocation (PA) issue is studied in multi-user three-cell systems under the degree-of-freedom (DoF) based transmission protocol. This protocol makes all the interferences received at each user orthogonal to the useful signal at the same user by Jafar's topological interference management through index coding, which is proved to offer full DoF. Under this protocol, we formulate the joint power allocations problem based on the objective of energy efficiency under the required quality-of-service constraint. Due to the highly complicated Lagrangian equation, the properties of Lambert function are widely exploited to solve the problem using a closed-form expression. It is discovered that the relationship among the optimal power coefficients are completely different from that of the well-known water-filling method. Simulations demonstrate the energy efficiency of the designed scheme.

In this letter, we propose two robust and distributed game-based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [1] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.

Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.

Considering worse-case channel uncertainties, we investigate the robust energy efficient (EE) beamforming design problem in a K-user multiple-input-single-output (MISO) interference channel. Our objective is to maximize the worse-case sum EE under individual transmit power constraints. In general, this fractional programming problem is NP-hard for the optimal solution. To obtain an insight into the problem, we first transform the original problem into its lower bound problem with max-min and fractional form by exploiting the relationship between the user rate and the minimum mean square error (MMSE) and using the min-max inequality. To make it tractable, we transform the problem of fractional form into a subtractive form by using the Dinkelbach transformation, and then propose an iterative algorithm using Lagrangian duality, which leads to the locally optimal solution. Simulation results demonstrate that our proposed robust EE beamforming scheme outperforms the conventional algorithm.

In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

In this paper, we study the spectral efficiency of the uplink multi-user large-scale distributed antenna systems (DAS) with imperfect channel state information. We propose the system model of multi-user DAS and illustrate the necessity of pilot reuse. Then, we derive the sum-rate of the system under pilot contamination. Furthermore, we investigate the asymptotical performance when the number of antennas goes to infinity. To reduce the pilot contamination, we present two novel pilot assignment algorithms to improve the spectral efficiency. Finally, we evaluate our proposed strategies through extensive simulations which show that compared with random pilot reuse, the min-max algorithm shows impressive performance with low complexity.

This paper considers a massive multiple-input-multiple-output (MIMO) relaying system with multi-pair single-antenna users. The relay node adopts maximum-ratio combining/maximum-ratio transmission (MRC/MRT) stratagem for reception/transmission. We analyze the spectral efficiency (SE) and power scaling laws with respect to the number of relay antennas and other system parameters. First, by using the law of large numbers, we derive the closed-form expression of the SE, based on which, it is shown that the SE per user increases with the number of relay antennas but decreases with the number of user pairs, both logarithmically. It is further discovered that the transmit power at the source users and the relay can be continuously reduced as the number of relay antennas becomes large while the SE can maintains a constant value, which also means that the energy efficiency gain can be obtained simultaneously. Moreover, it is proved that the number of served user pairs can grow proportionally over the number of relay antennas with arbitrary SE requirement and no extra power cost. All the analytical results are verified through the numerical simulations.

In this paper, the joint optimization issue of the cooperative precoder design is investigated for the transmission from the cooperative multi-point system to one mobile terminal. Based on the mean squared error minimization criterion, the problem is established for the cooperative precoder design. Unfortunately, this problem cannot be solved due to the block diagonal structure of the whole precoding matrix resulting from the fact that there is no data exchange among multiple base stations. In order to tackle this difficulty, the original problem is converted into an equivalent problem by stacking all of the nonzero entries in the block diagonal matrix into a long column vector. With the equivalent problem, the optimum solution is obtained in a closed-form expression by using the Lagrangian multiplier method. Numerical simulations illustrate the effectiveness of the proposed scheme in terms of bit error rate and spectral efficiency.

We propose an exponent-based partitioning broadcast protocol (EPBP) to promise the prompt dissemination of emergency message (EM) in vehicular networks. EPBP divides the communication range into segments with different widths iteratively. The width varies corresponding to the exponential curve. The design makes the farther no-empty segment thinner, as a result of which the collision rate of candidates' contention for the relay node decreases and the one-hop message progress increases efficiently. In addition, we adjust the interval of back-off timers to avoid the spurious forwarding problem, and develop more accurate analytical models for the performance. Our simulation verifies these models and show a significant increase of EPBP compared with the state-of-the-art protocols. EM dissemination speed can be improved as 55.94% faster in dense vehicle networks, and packet delivery ratio has risen to higher than 99.99%.