In this paper, we investigate the signal detection for user-centric network (UCN) massive multi-input multi-output (mMIMO) system. We consider that the users are divided into multiple user groups (UGs). For each UG, leveraging the interference sparsity, we reveal that the performance of the minimum-mean-square-error (MMSE) detector can be guaranteed in the network mMIMO system by using the matched filtering (MF) outputs of the intra-group and interfering users. Then, with the base station (BS) connection sparsity, we reveal that the detection performance of each UG is primarily determined by a limited number of associated BSs. To facilitate practical application, we propose a straightforward user grouping method and outline the process for determining interfering users and associated BSs for each UG in UCN mMIMO systems. Then, we propose a user-centric detection method that decouples the detection process for each UG into two stages. In the first stage, local MF is performed at each associated BSs using local information. In the second stage, group-wise interference cancellation (IC) is carried out to obtain detection results at the primary serving BS (PSBS) of each UG, with information exchanged from auxiliary serving BSs (ASBSs). Simulation results confirm the effectiveness and computational efficiency of our proposed user-centric detection for the UCN mMIMO system.

Optical wireless communication (OWC), with its abundant spectrum resources enabling ultra-high data rates, has emerged as a promising technique for the sixth generation (6G) wireless communications. To address the challenge of aligning base stations (BSs) and user terminals (UTs), as well as to enhance the number of served UTs and transmission rates per UT, this paper proposes a fiber-enabled network massive multiple-input multiple-output (MIMO) OWC system. By employing distributed passive optical antennas (POAs) comprised of fiber port arrays and lenses, BSs generate optical beams irradiating towards different directions, which can provide the optical signal coverage and spatial resolution of UTs at different positions, improving the system throughput. We establish the network channel model and design precoding vectors to maximize the system sum rate. We provide an iterative design of the precoding vectors in general case and propose an asymptotically optimal beam division multiple access (BDMA) transmission scheme with a large number of fiber ports. The simulation results demonstrate that our proposed system can achieve tens of Gbps per UT and several Tbps in system throughput. Finally, we construct an experimental system capable of achieving 10 Gbps transmission rate of each link and real-time wireless transmission of 4K video streams.

With the widespread application of location-based services, fingerprint-based localization has demonstrated advantages in environments with complex signal propagation. Deep learning has significantly improved the efficiency of both offline training and online matching in localization processes. However, existing fingerprints only contain terminal position information without capturing motion states, and neural network designs have not fully incorporated structural features such as fingerprint sparsity. In this paper, we propose a triple-beam fingerprint (TBF) incorporating Doppler information and design a Transformer-based localization and orientation awareness network (LOA-Net) to simultaneously estimate user position and motion direction in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first show the correlation between TBF and multipath information, and investigate the collinearity of different TBFs, demonstrating that TBF is an effective small-size sparse fingerprint. Then, we propose LOA-Net containing a mask-augmented detection Transformer for regression (MaskDETR-Reg) module and a fusion-enhanced Transformer for direction classification (Fusion-TDC) module to process angle-delay domain information and Doppler domain information, respectively. Finally, in the simulation of indoor scenarios defined in 3GPP 38.901, the proposed method achieves significantly better localization accuracy than weighted$K$-nearest neighbors (WKNN), 2D and 3D convolutional neural networks (CNNs), and achieves satisfactory motion direction estimation accuracy.

We investigate the linear precoding for sum-rate maximization in network massive multiple-input multiple-output (MIMO) transmission, where the cooperative transmission by all base stations (BSs) enhances the capacity, reliability, and robustness. To address the growing complexity of traditional iterative algorithms in large-scale systems, we leverage the weighted minimum mean square error (WMMSE) solution and show that the precoding vectors can be fully reconstructed from a set of low-dimensional parameters. By exploiting the structure and relationship of these parameters, we reformulate the original problem in a reduced-dimensional space while preserving equivalence to the original solution. Deep learning techniques are employed to solve this reformulated problem, where equivalent scaling of the variables facilitates pre-processing for training and further reduces the dimension of the learning input. A neural network is trained on the refined low-dimensional objectives with a tailored loss, allowing the precoding vectors to be directly calculated from its output. As demonstrated by numerical results, the proposed deep learning-based precoder performs well with considerably reduced online processing complexity.

Massive MIMO employs large antenna arrays to support multi-user wireless communications with high data rate. To mitigate the inter-user interference, various precoding schemes are proposed, which usually need iterative calculation and suffer from high complexity, especially for multi-cell scenarios. This paper investigates the intelligent design of network precoding schemes based on the optimal structure. Firstly, we derive an interference avoidance precoding in network massive MIMO communications, and propose an optimal precoding structure. We extract part expression in the optimal structure as parameters and propose a parameterized precoding without iterations. By using the convolutional neural network (CNN), these parameters can be trained and predicted, which significantly reduces the computational complexity. Simulation results present that the proposed precoding scheme can increase the transmission rate by 20% than the RZF scheme.

In the cell-free integrated sensing and communication (ISAC) system, distributed access points (APs) collaborate to simultaneously serve users and sense targets, which reduces the impact of signal blockage and enhances sensing accuracy. However, the cooperation among APs depends on backhaul links to exchange information with a central processing unit (CPU), which is often limited in practice. Therefore, this paper investigates beamforming design for cell-free ISAC multiple-input-multipleoutput (MIMO) systems with capacity-limited backhaul, aiming to minimize transmit power while ensuring both sensing and communication performance. Two algorithms are proposed: the global optimization algorithm and the low-complexity algorithm. The global optimization algorithm leverages the equivalence between backhaul capacity and AP-UE service pair selection, decomposing the original problem into multiple sub-problems. Each sub-problem is converted into a semidefinite programming (SDP). By selecting the best solution from all sub-problems, the globally optimal solution can be achieved. In the low-complexity algorithm, the backhaul capacity constraint is approximated by a smooth function. Successive convex approximation (SCA) is then applied to address the resulting non-convex constraint iteratively. Simulation results validate the effectiveness of the proposed algorithms.

Massive MIMO technology can meet the increasing demand for ultra-high transmission rate in modern wireless networks. However, interference among users sharing the same resource block (RB) reduces the transmission rate, and restricts the capabilities of massive MIMO systems. In this paper, we propose a multi-cell user grouping method based on graph clustering to enhance the system performance. We provide a beam based statistical channel model and establish a user grouping problem aimed at maximizing the sum rate. We analyze user grouping criteria that consider channel power and directions for users on the same RB. Subsequently, we employ unsupervised learning to develop a density-cut-based graph clustering algorithm (DGCA). Simulation results validate that our algorithm achieves a higher sum rate than the benchmark methods without extra computational complexity.

Multiple-input-multiple-output (MIMO) optical wireless communication (OWC) is a promising technology capable of providing high data rates. However, interuser interference can negatively impact system performance. Existing studies employ CVX-based beamforming designs to mitigate this issue, but these approaches suffer from high computational complexity. To tackle this challenge, this article studies the low-complexity beamforming design to maximize the sum rate. Specifically, we first utilize the fractional programming (FP) method to decompose the original beamforming problem into a series of convex subproblems and propose a block coordinate descent (BCD)-based beamforming framework. Then, we provide closed-form or semi-closed-form solutions for these subproblems. For the general approximate sum rate maximization, we drive the optimal semi-closed-form beamforming structure based on the Karush-Kuhn–Tucker (KKT) conditions. Furthermore, we propose a closed-form solution for the upper bound maximization by taking advantage of the separability of optical power constraints. Finally, numerical results indicate that the proposed beamforming designs reduce the computational complexity significantly while keeping the sum rate performance.

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of base stations (BSs) instead of all the BSs, facilitating the implementation of the system and lowering the dimension of the precoders to be designed. By proving that the precoder set satisfying the per-BS power constraints forms a Riemannian submanifold of a linear product manifold, we transform the constrained precoder design problem in Euclidean space to an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices, which is beneficial in practice. The complexity analyses show the high computational efficiency of RCG precoder design. Simulation results demonstrate the numerical superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.

In high-speed railway (HSR) communications, the channel suffers from severe Doppler and channel aging effects caused by the high mobility, making the channel outdated quickly. To address this issue, we investigate the robust precoder design against channel aging and prediction inaccuracy in massive multiple-input multiple-output (MIMO) systems with matrix manifold optimization. First of all, we introduce the concept of the quadruple beams (QBs), and establish a QB based channel model with sampled quadruple steering vectors. Then, the upcoming space domain channel of interest can achieve a higher accuracy by channel prediction with the estimated QB domain channel. To further improve the performance while save the pilot overhead, we predict the forthcoming QB domain channel and integrate the prediction inaccuracy within the a posterior QB domain statistical channel model. Then, we consider the robust precoder design aiming to maximize the upper bound of the ergodic weighted sum-rate (WSR) on the Riemannian submanifold formed by the precoders satisfying the total power constraint (TPC). Riemannian ingredients are derived for matrix manifold optimization, with which the Riemannian conjugate gradient (RCG) method is proposed to solve the unconstrained problem on the manifold. The RCG method mainly involves the matrix multiplication and avoids the need of matrix inversion of the transmit antenna dimension. The simulation results demonstrate the effectiveness of the proposed channel model and the superiority of the RCG method for robust precoder design against channel aging and prediction inaccuracy.

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of the base stations (BSs) instead of all BSs, lowering the dimension of the precoders to be designed. Each BS in the system has a power constraint. By proving that the precoder set satisfying the constraints forms a Riemannian submanifold, we transform the constrained precoder design problem in Euclidean space as an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices. The complexity analyses show the high efficiency of RCG precoder design. Simulation results demonstrate the superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.

Symbol-level precoding exploits the symbol constellation structure and transforms multi-user interference into a useful signal by taking into account both channel state information and data symbols, which has recently emerged as a novel paradigm for future wireless communications. The symbol-level precoding highly depends on the modulation and less works consider PAM and QAM constellations. In this paper, we focus on PAM and QAM constellations and propose a symbol-level precoding design to minimize the symbol error probability (SEP). We first analyze the SEP for PAM and QAM symbols and derive an upper bound of SEP. To minimize the SEP for all users, we directly design the transmitted signal, which is a linear combination of channel vectors. In addition, to reduce the complexity brought by the SEP function, we further relax the SEP expression and propose a near-optimal solution of the transmitted signals. Simulation results demonstrate that the proposed scheme achieves superior performance than other existing precoding schemes in terms of SEP.

In this paper, we investigate multi-cell resource management for massive MIMO communications, where users within different cells reuse the same time-frequency resource further aggravating the inter-user interference. Based on re-inforcement learning, which has been a useful technique for resource management, we propose a joint user grouping and time-frequency resource allocation algorithm (JUGRA). JUGRA contains user grouping network (UGNet) and time-frequency resource allocation network (TFRANet). TFRANet is used to select time-frequency resource allocation scheme, which maximizes system throughput with fixed user grouping scheme. UGNet is built to select the multi-cell user grouping scheme and calls TFRANet to evaluate the system performance, realizing joint optimization of user grouping and resource allocation. Simulation results demonstrate that the performance of JUGRA can be improved by more than 30% over the greedy algorithm with lower computational complexity. The proposed time-frequency resource allocation algorithm based on TFRANet can achieve 95 % of exhaustive search performance.

The capacity region for single input single out broadcast channel (SISO BC) is achieved by non-orthogonal multiple access (NOMA), which utilizes successive interference cancellation (SIC) to mitigate the inter-user interference. However, the complexity of SIC is high. To balance between the sum rate performance and the complexity of receivers, in this paper, we explore rate splitting multiple access (RSMA) in visible light communications (VLC). We formulate the joint rate allocation and power control problem to maximize the sum-rate under both quality of service (QoS) and SIC constraints. To solve this non-convex problem, a successive convex approximation (SCA) based algorithm is proposed to obtain a local optimal solution. Numerical results show that 1-layer RSMA is able to achieve very close performance to NOMA with much reduced complexity. Jointly considering the performance and complexity of the system, 1-layer RSMA is an attractive alternative to NOMA in SISO VLC networks.

This paper investigates the distributed precoding for network massive multi-input multi-output (MIMO) communications without data sharing between cells. In order to restrict the information exchange, which imposes significant requirements on signaling overhead, we first reformulate the original weighted sum-rate maximization problem into a cell-specific form. With this reformulated problem, we take a virtual weighted sumrate, whose expression only depends on precoders in a single cell and some initial values, as the objective function of an approximated problem. A stationary point of this non-concave virtual weighted sum-rate maximization problem is then achieved iteratively through the minorize-maximize (MM) algorithm. After exchanging a virtual covariance matrix generated locally, each base station (BS) can solely optimize its precoding matrix in parallel without any exchange during the optimization procedure. Numerical results show that the proposed method performs well in the sense of achievable sum-rate.

Physical-layer key generation (PKG) exploits the reciprocity and randomness of wireless channels to generate a symmetric key between two legitimate communication ends. However, in multi-cell systems, PKG suffers from severe pilot contamination due to the reuse of pilots in different cells. In this paper, we invoke multiple reconfigurable intelligent surfaces (RISs) for adaptively shaping the environment and enhancing the PKG performance. To this end, we formulate an optimization problem to maximize the weighted sum key rate (WSKR) by jointly optimizing the precoding matrices at the base stations (BSs) and the phase shifts at the RISs. To address the non-convexity of the problem, we adopt an alternating optimization (AO)-based algorithm that divides the joint optimization problem into two subproblems. For the subproblem of precoding matrices, we apply the Lagrangian dual approach based on the Karush-Kuhn-Tucker (KKT) conditions. As for the subproblem of phase shifts, we adopt a projected gradient ascent (PGA) algorithm. Simulation results validate the effectiveness of the proposed scheme, demonstrating significant gains in WSKR. Moreover, compared with a single-RIS case, deploying multiple RISs offer spatial diversity so as to improve the PKG performance of multicell systems.

Channel reciprocity-based key generation (CRKG) has recently emerged as a new technique to address the problem of key distribution in wireless networks. However, as this approach relies upon the characteristics of fading channels, the corresponding secret key rate may be low when the communication link is blocked. To enhance the applicability of CRKG in harsh propagation scenarios, this paper introduces a novel multiuser key generation scheme, which is referred to as RIS-assisted multiuser key generation (RMK) that leverages the reconfigurable intelligent surface (RIS) technology for appropriately shaping the environment and enhancing the sum secret key rate between an access point and multiple users. In the RMK scheme, an RIS-induced channel, rather than the direct channel, serves as the key source. We derive a general closed-form expression of the secret key rate and optimize the configuration of the RIS to maximize the sum secret key rate over independent and correlated fading channels in the presence of multiple users. In the presence of independent fading, we introduce a low-complexity algorithm based on the Karush-Kuhn-Tucker (KKT) condition. In the presence of correlated fading, the optimization problem is non-convex and challenging to solve. To tackle it, we propose a new optimization algorithm based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) methods. Simulation results demonstrate that the proposed RMK scheme outperforms existing RIS-assisted algorithms and achieves a near-optimal sum secret key rate over independent and correlated fading channels.

Distributed computing systems, such as Hadoop, have been widely studied and used for executing and analyzing large data. In this paper, we investigate an emerging resource allocation problem for wireless distributed computing systems consisting of multifunctional nodes in charge of both numerical computation and wireless communication with master nodes. We focus on a computation power consumption model based on CMOS devices and a communication power consumption model involving multiple antenna transceivers against mutual interference. We present a joint optimization problem for workload scheduling and power allocation for achieving maximum computational speed under total power constraint. We simplify the joint optimization into two sub-problems. For workload scheduling as an integer programming sub-problem, we relax the integer constraint and establish the equivalence between relaxed and original problems. For the power allocation sub-problem, we maximize a difference of convex functions by utilizing the concave-convex procedure. We prove our proposed algorithm to converge to a stationary point of the original program. Simulation results confirm the efficiency and near-optimal performance of our proposed algorithms.

This work proposes a fiber-enabled optical wireless communication (FE-OWC) system for bidirectional communications between the base station (BS) and a number of mobile user terminals (UTs) via full beam coverage, aimed at facilitating ultra-high data rate communications. The FE-OWC system comprises optical antennas, optical chains, and baseband units at both BS and UTs. The innovative optical antenna consists of an array of fiber ports and a transceiver lens, which can form a number of transmit and receive optical beams and provide a full beam coverage for simultaneous downlink and uplink connections with a number of UTs, respectively. We present analysis to characterize downlink and uplink channel models and gains, including both optical and electrical parts, between the BS and UTs and conduct a complete link budget analysis. We further design downlink and uplink multiuser multiple-input multiple-output (MIMO) as well as massive MIMO transmission protocols and develop asymptotically optimal schemes for a large number of fiber ports. Numerical results illustrate that the FE-OWC system has the potential to support over 10 Gbps data rate per UT and Tbps system throughput required in future 6G mobile communication systems.

Physical-layer key generation (PKG) based on channel reciprocity has recently emerged as a new technique to establish secret keys between devices. Most works focus on pairwise communication scenarios with single or small-scale antennas. However, the fifth generation (5G) wireless communications employ massive multiple-input multiple-output (MIMO) to support multiple users simultaneously, bringing serious overhead of reciprocal channel acquisition. This paper presents a multi-user secret key generation in massive MIMO wireless networks. We provide a beam domain channel model, in which different elements represent the channel gains from different transmit directions to different receive directions. Based on this channel model, we analyze the secret key rate and derive a closed-form expression under independent channel conditions. To maximize the sum secret key rate, we provide the optimal conditions for the Kronecker product of the precoding and receiving matrices and propose an algorithm to generate these matrices with pilot reuse. The proposed optimization design can significantly reduce the pilot overhead of the reciprocal channel state information acquisition. Furthermore, we analyze the security under the channel correlation between user terminals (UTs), and propose a low overhead multi-user secret key generation with non-overlapping beams between UTs. Simulation results demonstrate the near-optimal performance of the proposed precoding and receiving matrices design and the advantages of the non-overlapping beam allocation.

Physical-layer key generation (PKG) in multi-user massive MIMO networks faces great challenges due to the large length of pilots and the high dimension of channel matrix. To tackle these problems, we propose a novel massive MIMO key generation scheme with pilot reuse based on the beam domain channel model and derive close-form expression of secret key rate. Specifically, we present two algorithms, i.e., beam-domain based channel probing (BCP) algorithm and interference neutralization based multi-user beam allocation (IMBA) algorithm for the purpose of channel dimension reduction and multi-user pilot reuse, respectively. Numerical results verify that the proposed PKG scheme can achieve the secret key rate that approximates the perfect case, and significantly reduce the dimension of the channel estimation and pilot overhead.

The low cost and versatility of optical devices make it possible to pack a large number of optical transceivers into arrays and exploit massive multiple-input multiple-output (MIMO) transmission in optical wireless communications. Nevertheless, optical massive MIMO presents several distinct challenges such as, the line-of-sight propagation and intensity modulation, incompatible with existing radio frequency massive MIMO techniques. This paper presents a networked optical massive MIMO system that consists of multiple base stations (BSs), each equipped with a transmit lens and an optical transmitter array, cooperatively serving a number of user terminals (UTs), each equipped with a receive lens and a photodetector array. We establish the optical massive MIMO channel model, analyze its asymptotic behavior, and evaluate the potential of networked optical massive MIMO on system throughput improvement. To achieve high throughput, we propose optical beam division multiple access (BDMA) transmission schemes under the total and per transmitter power constraints with the asymptotic optimality. Our results show that the system sum rate increases proportionally to the number of BSs and UTs using the optical BDMA transmission. Further numerical results show that the proposed optical massive MIMO system along with the optical BDMA transmission is able to achieve high throughput with low complexity.

This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to$2K$and$K$under the total and per transmitter power constraints, respectively, where$K$is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam-domain optical massive MIMO communication approaches.

Random and high-agreement secret key generation from noisy wideband channels is challenging due to the autocorrelation inside the channel samples and compromised cross correlation between channel measurements of two keying parties. This paper studies the signal preprocessing algorithms to establish high-agreement uncorrelated secret key in the presence of channel independent eavesdroppers. We first propose a general mathematical model for various preprocessing schemes, including principal component analysis (PCA), discrete cosine transform (DCT) and wavelet transform (WT). Among preprocessing schemes, PCA is proved to achieve the optimal secret key rate. Next, PCA with common eigenvector has been found to outperform PCA with private eigenvector in terms of an overall consideration of key agreement, information leakage, and computational expense. Then, we propose a system level design of key generation, including quantization, information reconciliation, and privacy amplification. Numerical results verify that the key generation enhanced by PCA with common eigenvector can achieve secret key with high key generation rate, low key error rate, and good randomness.

We study a multiuser multicarrier downlink communication system in which the base station (BS) employs a large number of antennas. By assuming frequency-division duplex operation, we provide a beam domain channel model as the number of BS antennas grows asymptotically large. With this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate before developing necessary conditions to asymptotically maximize the upper bound, with only statistical channel state information at the BS. Inspired by these conditions, we propose a beam division multiple access (BDMA) transmission scheme, where the BS communicates with users via different beams. For BDMA transmission, we design user scheduling to select users within non-overlapping beams, work out an optimal pilot design under a minimum mean square error criterion, and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. The proposed BDMA scheme reduces significantly the pilot overhead, as well as, the processing complexity at transceivers. Simulations demonstrate the high spectral efficiency of BDMA transmission and the advantages in the bit error rate performance of the proposed pilot sequences.

We study multicarrier multiuser multiple-input multiple-output (MU-MIMO) systems, in which the base station employs an asymptotically large number of antennas. We analyze a fully correlated channel matrix and provide a beam domain channel model, where the channel gains are independent of sub-carriers. For this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate, based on which, we develop asymptotically necessary and sufficient conditions for optimal downlink transmission that require only statistical channel state information at the transmitter. Furthermore, we propose a beam division multiple access (BDMA) transmission scheme that simultaneously serves multiple users via different beams. By selecting users within non-overlapping beams, the MU-MIMO channels can be equivalently decomposed into multiple single-user MIMO channels; this scheme significantly reduces the overhead of channel estimation, as well as, the processing complexity at transceivers. For BDMA transmission, we work out an optimal pilot design criterion to minimize the mean square error (MSE) and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. Simulations demonstrate the near-optimal performance of BDMA transmission and the advantages of the proposed pilot sequences.

In this paper, the outage performance for a decode-and-forward two-way relay network is investigated in the presence of multiple interferers at the source terminals. The exact expression for the outage probability is derived and the disparity is discussed between symmetrical and asymmetrical cases. Based on the closed-form expressions the optimal power allocation between source terminals is discussed and the diversity order is derived. The effect of interference power is also studied. Simulation results are provided to validate the analytical results.