As the dense deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems presents significant challenges, per-AP coverage can be expanded using large-scale antenna arrays (LAAs). However, this approach incurs high implementation costs and substantial fronthaul demands due to the need for dedicated RF chains for all antennas. To address these challenges, we propose a hybrid beamforming framework that integrates wave-domain beamforming via stacked intelligent metasurfaces (SIM) with conventional digital processing. By dynamically manipulating electromagnetic waves, SIM-equipped APs enhance beamforming gains while significantly reducing RF chain requirements. We formulate a joint optimization problem for digital and wave-domain beamforming along with fronthaul compression to maximize the weighted sum-rate for both uplink and downlink transmission under finite-capacity fronthaul constraints. Given the high dimensionality and non-convexity of the problem, we develop alternating optimization-based algorithms that iteratively optimize digital and wave-domain variables. Numerical results demonstrate that the proposed hybrid schemes outperform conventional hybrid schemes, that rely on randomly set wave-domain beamformers or restrict digital beamforming to simple power control. Moreover, the proposed scheme employing sufficiently deep SIMs achieves near fully-digital performance with fewer RF chains in the high signal-to-noise ratios regime.

Stacked intelligent metasurfaces (SIMs), which are composed of multi-layer programmable metasurfaces, support beamforming in the wave domain, utilizing a limited number of radio frequency (RF) chains. This work investigates the application of SIMs in the downlink of cell-free massive multiple-input multiple-output systems with finite-capacity fronthaul links. Specifically, we address the joint optimization of fronthaul compression and hybrid digital and wave-domain beamforming. To tackle the resulting highly non-convex problem, an alternating optimization algorithm is proposed, which iteratively optimizes digital processing and wave beamforming variables. Numerical results demonstrate that the proposed method outperforms baseline schemes relying solely on digital or wave beamforming, achieving near fully-digital performance with a few RF chains, assuming a sufficiently high signal-to-noise ratio (SNR).

This work investigates a collaborative sensing and data collection system in which multiple uncrewed aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for each UAV and a common sensing task that is executed by all UAVs to enable cooperative transmission. Unlike existing studies, we explore the use of an advanced cell-free multiple-input-multiple-output (MIMO) network, which effectively manages inter-UAV interference. To further optimize wireless channel utilization, we propose a hybrid transmission strategy that combines time-division multiple access (TDMA), nonorthogonal multiple access (NOMA), and cooperative transmission. The problem of jointly optimizing task splitting ratios and the hybrid TDMA-NOMA-cooperative transmission strategy is formulated with the objective of minimizing mission completion time. Extensive numerical results demonstrate the effectiveness of the proposed task allocation and hybrid transmission scheme in accelerating the completion of sensing missions.

A sequential fronthaul network, referred to as radio stripes, is a promising fronthaul topology of cell-free MIMO systems. In this setup, a single cable suffices to connect access points (APs) to a central processor (CP). Thus, radio stripes are more effective than conventional star fronthaul topology which requires dedicated cables for each of APs. Most of works on radio stripes focused on the uplink communication or downlink energy transfer. This work tackles the design of the downlink data transmission for the first time. The CP sends compressed information of linearly precoded signals to the APs on fronthaul. Due to the serial transfer on radio stripes, each AP has an access to all the compressed blocks which pass through it. Thus, an advanced compression technique, called Wyner-Ziv (WZ) compression, can be applied in which each AP decompresses all the received blocks to exploit them for the reconstruction of its desired precoded signal as side information. The problem of maximizing the sum-rate is tackled under the standard point-to-point (P2P) and WZ compression strategies. Numerical results validate the performance gains of the proposed scheme.

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weighted bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.

Autoencoder (AE) techniques have been intensively studied for the optimization of wireless transceivers. However, fixed computational structures of existing AE models lack the flexibility to the lengths of message bits and codewords. This work proposes a versatile AE framework, termed by autoencoding graph neural network (AEGNN), where both encoder and decoder are realized by GNNs. The viability of the proposed AEGNN is demonstrated in various application scenarios.

A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource. By extremely high signaling overheads for both channel state information (CSI) acquisition and data sharing at a baseband unit (BBU), finding a joint transmission strategy with a significantly reduced signaling overhead is indispensable to achieve the cooperation gain in practical C-RANs. In this paper, we present a novel sparse joint transmission (sparse-JT) method for C-RANs, where the number of transmit antennas per unit area is much larger than the active downlink user density. Considering the effects of noisy-and-incomplete CSI and the quantization errors in data sharing by a finite-rate fronthaul capacity, the key innovation of sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming vectors, and power allocation to maximize a lower bound of the sum-spectral efficiency under the sparsity constraint of active RRHs. To find such a solution, we present a computationally efficient algorithm that guarantees to find a local-optimal solution for a relaxed sum-spectral efficiency maximization problem. By system-level simulations, we exhibit that sparse-JT provides significant gains in ergodic spectral efficiencies compared to existing joint transmissions.

This work studies the role of inter-user device-to-device (D2D) cooperation for improving physical-layer secret communication in multi-user downlink systems. It is assumed that there are out-of-band D2D channels, on each of which a selected legitimate user transmits an amplified version of the received downlink signal to other legitimate users. A key technical challenge for designing such systems is that eavesdroppers can overhear downlink as well as D2D cooperation signals. We tackle the problem of jointly optimizing the downlink precoding, artificial noise covariance, and amplification coefficients that maximize the minimum rate. An iterative alternating optimization algorithm is proposed based on the matrix fractional programming. Numerical results confirm the performance gains of the proposed D2D cooperation scheme compared to benchmark secret communication schemes.

This work studies the uplink of a multi-tenant cloud radio access network (C-RAN) system with spectrum pooling. In the system, each operator has a cloud processor (CP) connected to a set of proprietary radio units (RUs) through finite-capacity fronthaul links. The uplink spectrum is divided into private and shared subbands, and all the user equipments (UEs) of the participating operators can simultaneously transmit signals on the shared subband. To mitigate inter-operator interference on the shared subband, the CPs of the participating operators can exchange compressed uplink baseband signals on finite-capacity backhaul links. This work tackles the problem of jointly optimizing bandwidth allocation, transmit power control and fronthaul compression strategies. In the optimization, we impose that the inter-operator privacy loss be limited by a given threshold value. An iterative algorithm is proposed to find a suboptimal solution based on the matrix fractional programming approach. Numerical results validate the advantages of the proposed optimized spectrum pooling scheme.

Cloud radio access network (C-RAN) is an emerging architecture for achieving high data rate and massive connectivity in future wireless communication systems. To further improve the performance of C-RAN downlink, this work considers the advantages of cooperation among user equipments (UEs). Specifically, it is assumed that the UEs can exchange the downlink received baseband signals via device-to-device (D2D) communication links which are orthogonal to downlink channel. The problem of jointly optimizing the C-RAN downlink and inter-UeD2d communication strategies is tackled with the goal of maximizing the sum-rate of UEs. To address the formulated problem, an iterative algorithm is proposed which attains a suboptimal solution of the problem. Numerical results confirm that the inter-UE D2D cooperation is effective to improve the downlink performance of C-RAN systems.

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this letter proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL.

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.

This work deals with a joint downlink and uplink design for wireless powered cloud radio access network, where a baseband processing unit (BBU) communicates with downlink and uplink users through multiple remote radio heads (RRHs) connected to the BBU via finite-capacity fronthaul links. In the downlink, the RRHs send information to the downlink users and transfer energy to the uplink users (ULUs). By using the harvested energy, each ULU transmits information to the BBU through the uplink channels. In this work, we maximize the uplink sum-rate of the ULUs subject to the minimum downlink rate constraint as well as the per-node transmit power and the fronthaul capacity constraints. Numerical results confirm the advantages of the proposed algorithm compared to baseline schemes.

This work proposes an energy-efficient design of transmit power control and relaying strategies for the uplink of a cloud radio access network (C-RAN) with wireless fronthaul link. In the system, a set of single-antenna user equipments (UEs) send independent messages to a multi-antenna baseband processing unit (BBU) through a set of parallel multi-antenna remote radio heads (RRHs). The radio access link from the UEs to the RRHs is assumed to be orthogonal to the fronthaul link from the RRHs to the BBU. Since the overall performance of the system may be limited by the battery constraints of the UEs, this work tackles the problem of maximizing the worst energy efficiency of the UEs subject to the transmit power constraints at the UEs and the RRHs. Numerical results are provided to validate the advantages of the proposed energy-efficient scheme.

This work studies distributed uplink reception for a device-to-device (D2D) underlaid cloud radio access network (C-RAN) system, in which cellular user equipments (UEs) communicate with a baseband processing unit (BBU) through a set of distributed remote radio heads (RRHs) that are connected to the BBU via finite-capacity fronthaul links. The uplink of C-RAN system is interfered by a number of D2D communication links that operate in the same frequency band. Most of prior works on C-RAN uplink systems prescribe that the noise signals of different RRHs do not have statistical correlation. However, in the D2D underlaid C-RAN uplink systems, the effective noise signals that contain the D2D interference signals may have inter-RRH cross correlation. This work proposes an improved distributed reception scheme which leverages the noise correlation information. To tackle non-convex sum-rate maximization problem, a successive convex approximation (SCA) based iterative algorithm is derived that guarantees monotonically non-decreasing sum-rates. Some numerical results are presented that validate the advantages of the proposed scheme.

This work studies the uplink of a cloud radio access network (C-RAN) coexisting with a set of in-band device-to-device (D2D) communication links. In the system, the radio access communication from the user equipments (UEs), served by the C-RAN system, to the remote radio heads (RRHs) and the D2D communications take place at the same frequency band so that they interfere with each other while degrading the overall spectral efficiency performance. To mitigate this impact, the joint design of C-RAN uplink power control, D2D transmit power control as well as fronthaul quantization strategies is tackled with the goal of maximizing the sum-rate of all the UEs of the C-RAN and D2D communication systems. For the fronthaul quantization, both the Gaussian test channel and uniform scalar quantization approaches are considered. An iterative algorithm is derived based on the concave convex procedure (CCCP) approach to find an efficient solution, and numerical results are provided to examine the advantages of the proposed joint design as compared to the conventional separate optimization algorithms.

This study addresses the achievable sum-rate for the uplink of a cloud radio access network (C-RAN) operating in a linear Wyner-type topology, i.e., with partial channel connectivity. In the system, the radio units (RUs) communicate with a central, or cloud, unit (CU) by means of digital finite-capacity fronthaul links. The messages sent by the user equipments (UEs) are jointly decoded by the CU based on the compressed baseband signals received on the fronthaul links. Unlike prior works, each RU is assumed to be also connected to its neighboring RUs via finite-capacity fronthaul links. Under the standard assumption that the RUs do not perform channel decoding (i.e., oblivious RUs), each RU performs in-network processing of the uplink received signal and of the compressed baseband signal received from the adjacent RU, with the CU carrying out channel decoding. A closed-form expression of the achievable sum-rate is derived assuming point-to-point compression, and then analytical expressions are provided for more advanced fronthaul compression schemes that leverage side information. Numerical examples provide insights into the advantages of inter-RU communications and into the performance gap to existing sum-rate upper bounds.

This paper considers the maximization of total throughput and device lifetime for point-to-point multiple-input multiple-output communication systems, where a transmission node has a battery which exhibits non-linear battery discharge behaviors. We adopt a battery model called Peukert's law to render the non-linear battery characteristics and formulate the battery constraint from Peukert's law. Then, we prove that the total throughput is a strictly concave function in terms of the battery lifetime under the battery constraint. Also, we derive the optimality conditions for the total throughput maximization and device lifetime maximization problems from the strict concavity, and compute the optimal solutions by a bi-section method. Furthermore, we provide a solution based on asymptotic analysis for the case, where the eigenvalue distribution of the channel matrix is not available. In the simulation section, we conduct accurate battery discharge simulations to validate this paper. We confirm that the analysis matches well with the battery simulations, and the derived optimal scheme outperforms the baseline schemes, which neglect the non-linear battery discharge properties. Also, the proposed solution based on asymptotic analysis is shown to have almost the same performance compared with the optimal solution.

This paper studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, as in cloud-RAN (C-RAN), a baseband processing unit (BBU) can perform joint baseband processing on behalf of the remote radio heads (RRHs) that are connected to the BBU by means of the fronthaul links. In addition to the minimal functionalities of conventional RRHs in C-RAN, the RRHs in an F-RAN may be equipped with local caches, in which frequently requested contents can be stored, as well as with baseband processing capabilities. They are hence referred to as enhanced RRH (eRRH). This paper focuses on the design of the delivery phase for an arbitrary pre-fetching strategy used to populate the caches of the eRRHs. Two fronthauling modes are considered, namely, a hard-transfer mode, whereby non-cached files are communicated over the fronthaul links to a subset of eRRHs, and a soft-transfer mode, whereby the fronthaul links are used to convey quantized baseband signals as in a C-RAN. Unlike the hard-transfer mode in which baseband processing is traditionally carried out only at the eRRHs, the soft-transfer mode enables both centralized precoding at the BBU and local precoding at the eRRHs based on the cached contents, by means of a novel superposition coding approach. To attain the advantages of both approaches, a hybrid design of soft- and hard-transfer modes is also proposed. The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to compare the performance of hard- and soft-transfer fronthauling modes, as well as of the hybrid scheme, for different baseline pre-fetching strategies.

This letter studies a joint design of fronthaul and radio access links for a cloud radio access network (C-RAN) with wireless fronthauling, where a baseband unit (BBU) controls a number of remote radio heads (RRHs) via wireless fronthaul links to communicate with user equipments. We first review a basic approach based on a single cell concept, whereby the RRHs operate as decode-and-forward (DF) relays. Then, a cooperative transmission from the RRHs based on decompress-and-forward (DCF) relaying is proposed, which is a standard concept in C-RAN. For both strategies, a problem of jointly optimizing the BBU and RRH operations is tackled with the goal of maximizing the weighted sum-rate subject to the BBU and per-RRH power constraints. For each formulated problem, an iterative algorithm is derived that achieves a sequence of monotonically nondecreasing objective values at each iteration. It is confirmed via numerical results that the DCF-based cooperative scheme significantly outperforms the DF-based single-cell approach.

This letter studies the robust design of downlink precoding for cloud radio access network (C-RAN) in the presence of asynchronism among remote radio heads (RRHs). Specifically, a C-RAN downlink system is considered in which nonideal fronthaul links connecting two RRHs to a baseband unit (BBU) may cause a time offset, as well as a phase offset, between the transmissions of the two RRHs. The offsets are a priori not known to the BBU. With the aim of counteracting the unknown time offset, a robust precoding scheme is considered that is based on the idea of correlating the signal transmitted by one RRH with a number of delayed versions of the signal transmitted by the other RRH. For this transmission strategy, the problem of maximizing the worst-case minimum rate is tackled while satisfying per-RRH transmit power constraints. Numerical results are reported that verify the advantages of the proposed robust scheme as compared to the conventional nonrobust design criteria as well as noncooperative transmission.

In the downlink of cloud radio access networks, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art schemes, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals for different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate over precoding and compression strategies is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on independent compression across the BSs.

This work investigates efficient backhaul compression strategies for the uplink of cloud radio access networks with a general multihop backhaul topology. In these systems, each radio unit (RU) communicates with the managing control unit (CU) through a set of intermediate RUs. A baseline multiplex-and-forward (MF) scheme is first studied in which each RU forwards the bit streams received from the connected RUs without any processing. It is observed that this strategy may cause significant performance degradation in the presence of a dense deployment of RUs. To obviate this problem, a scheme is proposed in which each RU decompresses the received bit streams and performs linear in-network processing of the decompressed signals. For both the MF and the decompress-process-and-recompress (DPR) backhaul schemes, the optimal design is addressed with the aim of maximizing the sum-rate under the backhaul capacity constraints. Based on the analysis, numerical results are provided to compare the performance of the MF and DPR schemes, highlighting the potential advantage of in-network processing.

This work considers a setting in which an encoder wishes to communicate with a decoder through a relay that is connected to the decoder via a finite-capacity link. Motivated by communication on the uplink of a cloud radio access cellular network, it is assumed that the relay compresses and forwards the received signal; moreover, the decoder has side information about the transmitted signal that is subject to fading whose realization is unknown to encoder and relay. A robust transmission and compression strategy is proposed that aims at minimizing the transmitted power under competitive rate optimality constraints. This contrasts with more conventional worst-case or average performance criteria. The transmission strategy is based on a broadcast coding and is parameterized by the maximum tolerable delay in terms of number of fading coherence blocks. Numerical results demonstrate the role of delay and the advantages of broadcast coding over the conventional single-layer transmission.

This paper addresses robust communication on a fading relay channel in which the relay is connected to the decoder via an out-of-band digital link of limited capacity. Both the source-to-relay and the source-to-destination links are subject to fading gains, which are generally unknown to the encoder prior to transmission. To overcome this impairment, a hybrid automatic retransmission request (HARQ) protocol is combined with multi-layer broadcast transmission, thus allowing for variable-rate decoding. Moreover, motivated by cloud radio access network applications, the relay operation is limited to compress-and-forward. The aim is maximizing the throughput performance as measured by the average number of successfully received bits per channel use, under either long-term static channel (LTSC) or short-term static channel (STSC) models. In order to opportunistically leverage better channel states based on the HARQ feedback from the decoder, an adaptive compression strategy at the relay is also proposed. Numerical results confirm the effectiveness of the proposed strategies.

In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a “cloud” decoder through a set of multi-antenna base stations (BSs), which are connected to the cloud decoder through digital backhaul links of limited capacity. The BSs compress the received signal and send it to the cloud decoder, which performs joint decoding of the signals from all MSs. While the conventional solution prescribes that the cloud decoder performs first decompression and then decoding, recent work has shown that potentially larger rates can be achieved with joint decompression and decoding (JDD) at the cloud decoder. The sum-rate maximization problem with JDD, under the assumption of Gaussian test channels, is shown here to be an instance of a class of non-convex problems known as Difference of Convex (DC) problems. Based on this observation, an iterative algorithm based on the Majorization Minimization (MM) approach is proposed that guarantees convergence to a stationary point of the sum-rate maximization problem. Numerical results demonstrate the advantage of the proposed algorithm compared to the conventional approach based on separate decompression and decoding.

In this paper, we study coordinated multi-cell downlink systems where multiple base stations jointly design a transmission strategy by sharing channel state information. Particularly, we tackle the signal-to-interference-plus-noise ratio (SINR) balancing problem to maximize the worst-user rate. We consider single-input single-output (SISO) interference channels (IFC) where all nodes are equipped with a single antenna, and there is one active user in each cell. First, achievable rate regions with symmetric complex (SC) and asymmetric complex (AC) signaling techniques are investigated. Then, we present the optimal and near-optimal SINR balancing algorithms with the SC signaling for two and three user SISO IFC. Due to residual interference, the worst-user rate of the SC signaling is saturated at high signal-to-noise-ratio region. To alleviate this problem, we also propose efficient balancing schemes based on the AC signaling for both two and three-user cases. Simulation results confirm effectiveness of the proposed SINR balancing algorithms and show that a substantial gain of the AC signaling is achieved over the SC signaling in terms of the maximum worst-user rate.

In this paper, we consider coordinated beamforming techniques where multiple base stations jointly design a transmission strategy by sharing channel state information. Particularly, we tackle the signal-to-interference-plus-noise ratio (SINR) balancing problem to maximize the worst-user rate for multi-cell downlink systems. To solve this problem, both symmetric complex (SC) and asymmetric complex (AC) signaling methods are investigated. First, we present the SINR balancing algorithm with the SC signaling. Due to residual interference, the worst-user rate in the SC signaling is saturated at high signal-to-noise ratio (SNR). To alleviate this issue, we also propose the SINR balancing technique based on the AC signaling which combines both interference alignment and power control methods. Simulation results confirm that the AC signaling outperforms the SC signaling scheme over all SNR range.

This work studies distributed compression for the uplink of a cloud radio access network, where multiple multi-antenna base stations (BSs) communicate with a central unit, also referred to as cloud decoder, via capacity-constrained back-haul links. Distributed source coding strategies are potentially beneficial since the signals received at different BSs are correlated. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information.

In this paper, we propose a linear precoding technique for weighted sum rate (WSR) maximization in multiple-input multiple-output interfering broadcast channels. In multicell environments, the WSR can be jointly maximized through centralized processing which causes a large amount of channel state information (CSI) exchange. In order to reduce the overhead associated with CSI, we focus on a distributed precoding scheme utilizing local CSI at each base station (BS). First, applying a high signal-to-interference-plus-noise ratio assumption, we decouple the WSR maximization problem into distributed problems. Then we solve this distributed WSR maximization problem for each BS by using a zero-gradient based algorithm which converges to a local maximum point. Unlike conventional distributed schemes which require additional information, our proposed scheme at each base station utilizes only the local CSI to compute its precoding matrices. Through the Monte-Carlo simulation, we show that our proposed algorithm exhibits the performance almost identical to the centralized scheme requiring the global CSI.

For multi-user interference channels (IC), an altruistic approach based on the zero-forcing (ZF) criterion shows the near-optimal performance at high signal-to-noise ratio (SNR), whereas its performance at low SNR becomes poor compared to a simple egoistic algorithm (selfish beamforming). Thus, balancing between the egoism and the altruism has been an important issue to achieve good sum-rate performance at overall SNR regime. In this paper, we propose a new approach for enhancing the performance by regularizing the ZF based transceivers. To this end, we start with investigating efficient ZF transceivers for 2-user and 3-user ICs. First, coordinated spatial multiplexing (CSM) is proposed for 2-user IC. For the 3-user case, it is shown that the enhanced interference alignment (E-IA) introduced in our previous work is the optimal ZF transceivers in terms of the sum-rate performance. Next, to improve the performance of the CSM and E-IA schemes at low SNR, we propose a non-iterative regularization method under the high SNR approximation. The distributed implementation of the proposed regularization method is also presented where each node is able to compute its own precoding or decoding matrix using local channel state information. From simulations, it is observed that the proposed regularized design outperforms the conventional schemes in overall SNR regime. Also, we confirm that our distributed approach provides a substantial performance gain over the conventional distributed scheme with reduced computational complexity.

In this paper, we study spatial degree-of-freedom (DOF) for two mutually interfering broadcast channels (IBC). As the demand for space-division multiple access (SDMA) increases, the IBC where each link has a single transmit node and multiple receive nodes becomes important. This paper presents the results of the lower and upper bounds on the DOF of the IBC. From the derived results, it is shown that for most cases, zero-forcing (ZF) beamforming can achieve the optimal DOF of the IBC except for some special cases. Also, we identify a condition that disabling receive cooperation in the multiple-input multiple-output interference channels causes no DOF loss. It is confirmed that we cannot expect a DOF improvement by enabling in-cell receive cooperation if in at least one of two BSs, the number of antennas is greater than or equal to that of users per cell. Furthermore, we observe a positive result that as the number of users goes to infinity, the total DOF of the IBC converges to the interference-free DOF, which is the maximum achievable DOF in the absence of the inter-cell interference.

In this letter, we study a linear beamforming technique for weighted sum-rate (WSR) maximization in multiple-input single-output interfering broadcast channels. We focus on a distributed beamforming scheme which utilizes local channel state information (CSI) to mitigate inter-cell interference. In order to decouple the WSR maximization problem which involves the beamforming vectors of all base stations (BSs) into a distributed WSR problem as a function of local CSI, we apply high signal-to-interference-plus-noise ratio approximation. After defining the distributed WSR function, we solve the decoupled problems by using a zero-gradient based algorithm which converges to a local optimal point. Unlike conventional distributed schemes where additional information should be exchanged at each iteration, each BS of the proposed scheme utilizes only the local CSI to compute its beamforming vectors. Also we prove the convergence of the proposed algorithm. Simulation results show that our proposed algorithm exhibits the WSR performance almost identical to the centralized scheme with substantially reduced overhead.

In this paper, we propose new beamforming techniques based on virtual signal-to-interference-plus-noise ratio (VSINR) for weighted sum-rate (WSR) maximization in coordinated multi-cell transmission. In earlier works based on the VSINR maximization, the parameters which control the interference power and the noise variance were set to fixed values regardless of channel realizations and the signal-to-noise ratio level. In order to obtain an improved WSR performance, we propose a method which adaptively adjusts the parameters after establishing a connection between the WSR and VSINR. Our proposed method can be applied to the cases of various coordination levels among base stations. To address practical implementation issues, a decentralized implementation of the beamforming techniques is also proposed based on local channel state information. Numerical results confirm that the proposed centralized schemes provide near-optimal WSR performance and the proposed decentralized methods show a negligible performance loss compared to the centralized algorithms with reduced system complexity.

In this letter, we consider a new interference alignment (IA) strategy for single-input single-output interference broadcast channels with constant channel coefficients. First, we show that 1.5 degrees of freedom (DOF) is achievable for 3-cell case by utilizing asymmetric complex signaling (AC) and multiuser diversity without symbol extension. It is also investigated that the achievable DOF varies with the user scaling condition and ω(√(SNR)) is required for guaranteeing the DOF of 1.5. To improve the sum-rate performance, user scheduling algorithms combined with the beamforming techniques are suggested which outperform the conventional IA schemes. After introducing an exhaustive scheduling algorithm which shows optimal sum-rate, a simplified scheduling method is also proposed which reduces both scheduling metric computations and the search size.

In this letter, we study multiple-input single-output joint processing (JP) systems with limited feedback where base stations exchange both channel state information and their data via ideal backhaul links. In order to optimize the sum-rate performance of the JP system, we propose a new feedback bit allocation scheme which maximizes quantization accuracy in the presence of pathloss. The quantization accuracy is formulated by the expectation of the inner product between the actual channel vector and the quantized channel vector. First, we derive the quantization accuracy as a closed form, which compensates the phase difference of two channels. Then, the maximum quantization accuracy is achieved by searching possible bit combinations. Simulation results show that the sum rate of our proposed feedback bit allocation strategy is more than twice compared to the conventional equal bit allocation method in the three cell case.

In this paper, we propose beamforming techniques based on virtual signal-to-interference-plus-noise ratio (VSINR) maximization for weighted sum-rate (WSR) maximization in multiple-input single-output (MISO) interference channels. In the earlier work by Zakhour and Gesbert, it was shown that all Pareto-optimal beamformers can be expressed as a solution to the VSINR problem. However, how to choose the weight coefficients in the VSINR expression is not addressed when solving the WSR maximization problem. Thus, we provide a method of computing the weight terms to achieve a certain desired WSR maximizing point. Since the beamforming vectors in the proposed scheme should be computed as a function of global channel state information (CSI), we also propose a decentralized approach which shows a performance close to the centralized scheme with a significant reduction in the CSI exchange overhead.

In this paper, we study multiple-input single-output joint processing (JP) systems with limited feedback where two adjacent base stations exchange both channel state information and their data. To optimize the sum-rate performance of the JP system, we propose a new feedback bit allocation method which maximizes quantization accuracy in the presence of pathloss. The quantization accuracy is formulated by the expectation of the inner product between the actual channel vector and the quantized channel vector. In order to maximize the quantization accuracy, we employ a new method which compensates the phase difference of the two channels. Through numerical evaluations, we show that our proposed feedback bit allocation strategies provide about 50% performance gain in terms of the sum rate performance compared to the conventional method with the equal bit allocation scheme.

In multi-cell environments, interference alignment (IA) introduced by Cadambe and Jafar is an effective strategy for managing interference. However, this requires a large number of symbol extension in time/frequency domain to gurantee the optimal number of degrees of freedom (DOF). Recently, in the single-input single-output (SISO) case, a new idea of the IA scheme based on asymmetric complex signaling and symbol extension was proposed where at least the DOF of 1:2 is achievable for all complex channel values. In this paper, we prove that at least the DOF of 1:5 is achievable in multi-cell and multi-user interfering broadcast channels under the assumption of constant channel coefficients with no symbol extension. We also show that the achievable DOF varies with the condition of user scale and ω(√(SNR)) users are required to guarantee the DOF of 1:5. Furthermore, a simple IA scheme in conjunction with user selection which groups semi-aligned users is proposed to realize the achievable DOF.

In this paper, we provide lower and upper bounds for the number of degree of freedom (DOF) of B multiple-input single-output (MISO) broadcast channels (BC) where each base station (BS) equipped with M antennas supports its corresponding K single antenna users suffering from inter-cell interference. The sufficient and necessary condition for tightness of two bounds is presented. From the derived result, it can be observed that in-cell receiver cooperation does not help in most of the cases in a multiple-input multiple-output (MIMO) interference channel (IFC) except for one special case. Even for that special case, the DOFs with and without in-cell receive cooperation approach the same value for large K. Also, in a MIMO IFC with symmetric antenna settings (i.e., M = K), if both transmit and receive cooperations are removed to make it a single-input single-output (SISO) IFC, we show that the DOF is not affected. In addition, the DOF is studied for two mutually interfering broadcast channels in the presence of a cognitive BS. We obtain an interesting result that disabling in-cell receive cooperation of the MIMO IFC causes no DOF loss if at least one of two transmitters is a cognitive BS.

In this letter, we propose an efficient algorithm which reduces the complexity of conventional vector perturbation schemes by searching the real and imaginary components of a perturbation vector individually. To minimize a performance loss induced from the decoupled joint search, we apply diagonal precoding at the transmitter whose parameters are iteratively optimized to maximize the chordal distance between subspaces spanned by the real and imaginary components. We also propose a simple non-iterative method with a slight performance loss which can achieve a significant complexity reduction compared to the conventional vector perturbation schemes.

In this paper, we introduce multi-link two-way relay channels where multiple two-way relay systems are interfering with each other. We study the capacity of this system by investigating the degree of freedom (DOF) with various message settings. Specifically, we consider two cases of two-way relay interference channels and two-way relay X channels. We show that the two-way relay interference channel where all nodes have M antennas obtains the DOF of 2M and compare with multipair two-way relay channels. Next, we introduce general message settings for two-way relay X channels. For the case where each user is equipped with 3 antennas and relays have 4 antennas, we prove that the DOF of 8 is achieved by employing network coding.

In this paper, we investigate an interference alignment (IA) technique introduced by Cadambe and Jafar in mutually interfering broadcast channels (IFBCs). First, we study the spatial multiplexing gain (SMG) for the 3-cell IFBC where all base stations and mobile users are equipped with multiple antennas. To achieve the derived optimal SMG, we extend the IA algorithm designed for K-user multi-input multi-output (MIMO) interference channels (IFCs) to the IFBC. In this paper, we present the IA scheme in conjunction with user selection which outperforms the time division multiple access (TDMA) technique in the IFBC environment. The optimal scheduling method capitalizes on multiuser diversity to achieve a significant fraction of sum capacity by using an exhaustive search algorithm. Since the computational complexity of the optimal scheduling method is prohibitive, a reduced complexity suboptimal scheduling method is proposed based on a coordinate ascent approach. Simulation results confirm that the reduced complexity scheduling algorithm achieves the sum rate close to the optimal algorithm with much reduced complexity.

In this paper, we consider a downlink transmission technique for multi-cell and multi-user interfering broadcast channels (IFBCs). In this IFBC model, the simplest way to manage inter-cell interference is an orthogonal access method, such as time-division multiple-access (TDMA). Recently, a novel idea of an interference management scheme named interference alignment (IA) has been introduced by Cadambe and Jafar, which requires symbol extension to guarantee a degree-of-freedom (DOF) gain. In this paper, we propose a new IA scheme without symbol extension which outperforms the TDMA and the conventional IA by exploiting multi-user diversity and asymmetric complex signaling. We first introduce an exhaustive scheduling algorithm which shows the optimum performance. Then we propose a simplified suboptimum method to reduce the computational complexity and the search size.

In this paper, we provide a precise expression of the spatial multiplexing gain (SMG) for two mutually interfering multiple-input multiple-output (MIMO) broadcast channels using linear transceiver, referred to as MIMO-IBC. The MIMOIBC has two base stations and K1, K2users, each equipped with multiple antennas, where independent messages are transmitted over fixed channels. We observe the variation of the SMG with respect to user antenna distribution, and compare the derived result to the SMG of the interference channel with full cooperation among users. Additionally, we propose a linear preceding and decoding scheme for the MIMO-IBC in terms of maximizing the total sum rate, by extending one designed for single-cell multiple-input single-output broadcast channels. Simulation results confirm the accuracy of our theoretical SMG analysis for the MIMO-IBC.

In this paper, we propose a new vector perturbation scheme which reduces the complexity associated with finding perturbation vectors by searching for the real and imaginary components individually. To minimize a performance loss induced from decoupling joint search, we apply diagonal preceding at the transmitter whose phase angles are iteratively optimized according to the criterion of maximizing the chordal distance between subspaces spanned by the real and imaginary components. Since our main objective is to reduce the system cost, we also propose a simple non-iterative method of finding phase angles with slight performance loss. Simulation results show that the proposed decoupled vector perturbation reduces the average number of search candidate by 56% in comparison to the original vector perturbation in 4-by-4 system. It is also noted that the simple choice of phase angles provides the performance almost identical to that of the iteratively optimized angles. Moreover, we develope a receive antenna combining method for the vector perturbation systems with multiple-antenna receivers. The proposed algorithm designs receive combiners based on the criterion of optimizing the performance metric via a block-coordinate ascent approach.

The original interference alignment (IA) scheme provides poor sum-rate performance compared to simple orthogonal access schemes such as time-division multiple access (TDMA) in low-to-medium SNR under total power constraint. In this paper, we address this problem by proposing a method of regularizing the IA scheme with a criterion of minimizing the weighted sum of the mean square error (WMSE) function. To perform the regularization process efficiently, the weight terms in the WMSE metric should be computed from the optimal zero-forcing (ZF) schemes. Thus, we first prove the optimality of the enhanced IA algorithm introduced in our previous work in the ZF sense. From simulation results, it is shown that the proposed scheme outperforms the TDMA in overall SNR regime. We can further improve the performance by repeating the proposed regularization process iteratively. Moreover, we propose a modified design that provides robustness in the presence of channel uncertainty.

In this paper, we consider the network multipleinput multiple-output (MIMO) where the base stations (BS) exchange only channel state information (CSI) to jointly design their transmission strategy. We particularly focus on a signal-tointerference-plus-noise ratio (SINR) balancing problem. First, the achievable rate regions with symmetric complex (SC) and asymmetric complex (AC) signaling techniques are invetigated. It is observed that the AC signaling shows a substantial gain over the SC signaling in terms of maximizing the worst-user rate as the system signal-to-noise-ratio (SNR) increases. After establishing the optimal SINR balancing algorithm with the SC signaling, we confirm the effectiveness of the AC signaling by proposing an efficient balancing scheme which outperforms the SC signaling scheme over all SNR regime.

In this letter, we present an improved precoding technique which reduces a modulo loss in vector perturbation with low complexity. Instead of searching perturbation vectors in the infinite lattice, the proposed scheme restricts the search range by utilizing the distribution of the perturbation vector depending on transmitted data. As a result, we can achieve significant complexity savings at the transmitter while providing better performance compared to the original vector perturbation.

In this letter, we provide an expression of spatial multiplexing gain (SMG) for two mutually interfering multiple-input multiple-output (MIMO) broadcast channels, referred to as MIMO-IBC, with linear transceiver. We derive the SMG with respect to user antenna distribution, and compare the systems with and without cooperation among receive antennas in each cell. Additionally, we propose a linear precoding and decoding algorithm for the MIMO-IBC which maximizes the sum rate by extending a solution for single-cell multiple-input single-output broadcast channels. Simulation results confirm the accuracy of our theoretical SMG analysis for the MIMO-IBC.

This paper studies linear precoding and decoding schemes for K-user interference channel systems. It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves a theoretical bound on degrees of freedom (DOF) for interference channel systems. Based on this, we first introduce a non-iterative solution for the precoding and decoding scheme. To this end, we determine the orthonormal basis vectors of each user's precoding matrix to achieve the maximum DOF, then we optimize precoding matrices in the IA method according to two different decoding schemes with respect to individual rate. Second, an iterative processing algorithm is proposed which maximizes the weighted sum rate. Deriving the gradient of the weighted sum rate and applying the gradient descent method, the proposed scheme identifies a local-optimal solution iteratively. Simulation results show that the proposed iterative algorithm outperforms other existing methods in terms of sum rate. Also, we exhibit that the proposed non-iterative method approaches a local optimal solution at high signal-to-noise ratio with reduced complexity.

In this paper, we provide a lower and upper bound for the number of degrees of freedom (DOF) of B multiple-input single-output (MISO) broadcast channels (BC) where each base station (BS) equipped with M antennas supports its corresponding K single antenna users suffering from inter-cell interference. We show that two bounds meet with each other when M = 1 or M ¿ K or B > max(M,K)/min(M,K). From the derived result, we learn that the available DOF of the B-user multiple-input multiple-output (MIMO) interference channels is degraded by disabling receive cooperation if and only if M < K and B ¿ max(M,K)/min(M,K). We observe that even for that case, the multiple BCs where interference mitigation should be fully performed at the transmitters due to the distributed receive antennas can achieve the DOF more than 2/3 of the DOF of the MIMO interference channels. In addition, the exact number of DOF is derived for two mutually interfering and deterministic broadcast channels in the presence of cognitive BSs. Our result shows that in two BCs interfering with each other, whether receive cooperation exists or not, it does not affect the DOF if one of two transmitters is a cognitive BS.

In this paper, we propose a linear preceding and decoding scheme maximizing the sum rate of abuser interference channel systems where each node has multiple antennas. With an iterative approach, the preceding matrices are identified by deriving the gradient of the sum rate and applying the gradient descent method. Due to non-convexity of the formulated problem, the proposed precoder cannot guarantee the global optimal solution, and a locally maximized sum rate can be found by the proposed precoding scheme. Then, we obtain the single-symbol decodable receiver from the modified minimum mean-squared error filter. From simulation results, we exhibit a local optimal sum rate of the interference channel systems with the proposed method. Also, we demonstrate that the proposed algorithm outperforms other existing methods in terms of the sum rate.

It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves the theoretical bound on degrees of freedom (DOF) for interference channel systems. However, since this method addresses the precoder design problem from the DOF point of view, some optimization processes are additionally needed in order to improve the sum rate performance. In this paper, based on the IA method, we propose a two-stage optimization of the precoding and decoding matrices in the interference channels. Simulation results show that the proposed method significantly improves the sum rate of the conventional IA scheme while maintaining the optimality of the DOF.

In this paper, we present an improved precoding technique which reduces a modulo loss in vector perturbation (VP) with low complexity for the downlink of a multiuser multiple-input multiple-output (MIMO) system. At low SNR regime, the VP suffers from the modulo loss due to the increased number of nearest neighbors. For the original VP, the sphere encoder searches perturbation vectors in the infinite lattice. In contrast, the proposed scheme restricts the search range utilizing the distribution of the perturbation vector depending on transmitted data. As a result, we can achieve significant complexity savings at the transmitter and the receiver while providing better performance compared to the conventional sphere encoder. Simulation results show that the proposed scheme provides a 0.2 dB gain over the conventional VP at a bit error rate (BER) of 10-3for the case of four transmit antennas and four users with 4QAM. Also, the proposed scheme reduces the maximum number of candidate search by 95% in comparison to the original VP.

In this paper, we derive a precise expression of spatial degrees of freedom (DOF) for two mutually interfering broadcast channels (IFBC) as a function of arbitrary numbers of transmit antennas and users. The lower bound on the DOF is obtained by showing that the zero-forcing solution suffices to achieve all the DOF. Also, the upper bound which coincides with the lower bound is shown using Jafar's earlier work. From the derived result, we observe that disabling receive cooperation of the MIMO interference channel causes the DOF loss. Additionally, we propose a linear precoding scheme for the IFBC by extending one designed for broadcast channels with an aim of maximizing the sum rate performance. We utilize the fact that the precoding matrices in stationary point always satisfy the zero-gradient condition. Our result is confirmed through numerical simulations on the sum rate performance of the proposed precoding technique.

This letter proposes a new optimal ordering method which minimizes error propagation in the vertical Bell-lab layered space-time (V-BLAST) by exploiting the whole filter output. A suboptimal ordering metric is also proposed which requires much reduced complexity compared to the optimal ordering metric. We also derive a simplified version of the suboptimal ordering metric which achieves a significant performance gain over the conventional ordering with minor additional complexity.

In this paper, we present a new precoding technique using rotation transformations for closed loop multiple-input multiple-output (MIMO) wireless systems, which does not require the singular value decomposition (SVD) operation of the channel transfer matrix and allows a simple maximum-likelihood (ML) decoding at the receiver. We divide the precoding process into two steps: orthogonalization transformation which induces orthogonality between transmitted signals and beamforming transformation which achieves diversity gain. In the proposed method, we utilize a design criterion based on the minimum Euclidean distance between the received signals and then the vector orthogonalization is connected to the vector-norm maximization. In this paper, we focus on spatial multiplexing systems transmitting two independent data streams. Compared with the SVD based schemes, the proposed approach maintains a low complexity by relying only on three different kinds of rotation matrices for both the orthogonalization and beamforming transformation. Simulation results confirm that the proposed two step precoding achieves the better performance than the conventional SVD based MIMO precodings with reduced complexity.

This paper proposes an efficient beamforming scheme which attains optimality as singular value decomposition (SVD) based systems with low complexity utilizing transmit maximum-ratio combining (TMRC) techniques. The TMRC scheme is the optimum structure for single beamforming systems in terms of received signal-to-noise ratio (SNR) in multiple-input single-output (MISO) channels. In this paper, we generalize the TMRC scheme to multiple beamforming multiple-input multiple-output (MIMO) systems which support more than one data stream in coded systems. We express each beamforming vector as a linear combination of TMRC vectors whose coefficients are optimized in a successive manner. Optimization of the beamforming vector is followed by the decorrelation process. All TMRC vectors used as a basis of the remaining beamforming vectors are made orthogonal to previously computed beamforming vectors. Exploiting the concept of the gradient ascent algorithm, we propose a simple non-iterative method of computing the precoder which obtains the near optimal performance. Also we derive a closed form expression of the output SNR distribution for the proposed scheme. Simulation results demonstrate that the proposed scheme achieves the almost identical link performance as the SVD-based system for arbitrary configurations with reduced complexity.

In this paper, we present a low-complexity method to generate a transmit beamforming vector for multiple-input-multiple-output (MIMO) systems. We begin by introducing new definitions regarding orthogonality between two complex valued vectors and then present new expressions of complex rotation matrices for the complex vector orthogonalization. The rotation matrices are utilized to derive the weight vector for the maximum-norm combining (MNC) process of two complex vectors, which provides a constructive basis for a new beamforming method. The proposed transmit beamforming method uses successive column combining of MIMO channel matrices based on MNC, and as a result, an approximate solution to the optimum beamforming vector is obtained. The proposed method offers a good tradeoff between complexity and performance. Simulation results demonstrate that the proposed beamforming method achieves the near-optimal performance with much reduced computational complexity, compared to the optimal beamforming scheme using singular-value decomposition (SVD) of the channel matrix.

This paper proposes a new precoding algorithm for orthogonalized spatial multiplexing (OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. The OSM scheme was recently introduced for closed-loop MIMO systems which allows single symbol decodability for maximum likelihood detection. To further improve the performance in OSM systems, we propose a new precoding method. For identifying the parameters of a precoder, we introduce a partitioning approach on the minimum Euclidean distance between constellation points in the effective channel. Also, it is shown that two real value parameters and one bit are required for feedback information in 4-QAM systems. Simulation results demonstrate that our precoding algorithm allows us to significantly improve the system performance with small increase of feedback values. We also confirm through simulations that the performance of the proposed scheme is the same as the optimum closed-loop MIMO systems.

In this paper, we present a new beamforming scheme for a downlink of multiuser multiple-input multiple- output (MIMO) communication systems. Recently, a block- diagonalization (BD) algorithm has been proposed for the multiuser MIMO downlink where both a base station and each user have multiple antennas. However, the BD algorithm is not efficient when the number of supported streams per user is smaller than that of receive antennas. Since the BD method utilizes the nullspace based on the channel matrix without considering the receive combining, the degree of freedom for beamforming cannot be fully exploited at the transmitter. In this paper, we optimize the receive beamforming vector under a zero forcing (ZF) constraint, where all inter-user interference is driven to zero. We propose an efficient algorithm to find the optimum receive vector by an iterative procedure. The proposed algorithm requires two phase values feedforward information for the receive combining vector. Also, we present another algorithm which needs only one phase value by using a decomposition of the complex general unitary matrix. Simulation results show that the proposed beamforming scheme outperforms the conventional BD algorithm in terms of error probability and obtains the diversity enhancement by utilizing the degree of freedom at the base station.

This paper proposes a low complexity unitary precoding scheme for multiple-input multiple-output (MIMO) systems. The singular-value decomposition (SVD) based transmission is capable of maximizing the system throughput when combined with power allocation and bit loading, and is known to be optimum in terms of capacity. This paper focuses on a system which attains the same optimality as the SVD-based system with low complexity utilizing transmit maximum-ratio combining (TMRC) techniques. The TMRC scheme is the optimum structure for single beamforming systems in terms of received signal-to-noise ratio (SNR) in multiple-input single-output (MISO) channels. In this paper, we generalize the TMRC scheme to multiple beamforming MIMO systems which supports more than one data stream in coded systems. Simulation results demonstrate that the proposed scheme achieves the almost identical link performance as the SVD precoding system for arbitrary configurations with reduced complexity.

In this paper, we propose a two-step precoding approach for multiple-input multiple-output (MIMO) systems by dividing the precoding process into two steps: spatial multiplexing transformation which establishes orthogonalized subchannels with the same quality and beamforming transformation which achieves diversity gain. In the proposed precoding method, we improve the system performance by maximizing the minimum Euclidean distance between the received signals. Compared with the optimal singular value decomposition based precoding methods, the proposed approach significantly reduces the processing complexity as well as the feedback overhead since precoding techniques are based on simple rotation transformations.

In this paper, we propose a new precoding algorithm for orthogonalized spatial multiplexing (OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. The OSM scheme was recently introduced for closed-loop MIMO systems which allows single symbol decodable maximum likelihood detection. To further improve the performance of the OSM system, we propose a new precoding method by maximizing the minimum Euclidean distance between constellation points in the effective channel. In order to efficiently identify the parameters of a precoder which maximizes the minimum distance, we introduce a partitioning approach. Through analysis, it is shown that one real value parameter and two bits are required for feedback information for precoding in 16-QAM systems. Simulation results demonstrate that our algorithm provides 9 dB and 7.5 dB gains at a bit error rate (BER) of 10-4over the conventional OSM systems for 4-QAM and 16-QAM, respectively. We also confirm that the performance of the proposed scheme is the same as that of the optimum closed-loop MIMO systems in terms of the minimum distance. Consequently, our precoding algorithm significantly improves the system performance with a small increase of feedback amount.

In this paper, we propose a new power allocation algorithm for orthogonalized spatial multiplexing (OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. Compared to SVD-based transmission scheme, the OSM scheme exhibits a good system performance with lower complexity and feedback overhead. To further improve the performance in OSM systems with power allocation, we introduce a geometric approach on the Euclidean distance between the constellation points in the effective channel. Using this approach, we show that the optimal power allocation parameters in terms of the minimum distance can be obtained. Simulation results demonstrate that our algorithm provides a 5 dB gain at a bit error rate (BER) of 10-4over that of no power allocation case with both QPSK and 16-QAM.

We propose a new transmit beam- forming technique for multiple-input-multiple-output (MIMO) systems to improve the link level performance. We present a method for orthogonalization of two complex-valued vectors by introducing a variation of the Jacobi rotations. We will show that based on the proposed rotation transformations, the orthogonality can be established among different complex-valued column vectors in the channel response matrix. Utilizing the orthogonality, we can achieve the channel gain comparable to the maximum singular value of the channel matrix. Simulation results demonstrate that the proposed beamforming scheme achieves the near-optimum performance with much reduced complexity and feedback overhead. Especially, for the two transmit antenna case, we show that the proposed beamforming scheme provides the optimal beamforming vector for MIMO systems.

In this paper, we propose a new transmit diversity technique for multiple-input multiple-output (MIMO) systems to improve the link level performance of open-loop systems over flat fading channels. By cyclically applying a predetermined set of precoding weight vectors, artificially induced fluctuation is created to achieve additional diversity gain in flat fading channels. To design the set of the precoding vectors, we exploit the knowledge on the distribution of near optimum precoding vectors observed in a beamforming scheme based on the rotation transformations. Simulation results demonstrate that the proposed open-loop diversity scheme with an arbitrary number of transmit antennas achieves a full diversity gain with computational complexity comparable to a single-input single-output (SISO) system.

This paper proposes a simple downlink transmit beamforming scheme for multiple-input multiple-output systems with low complexity. We first introduce the beamforming system where the base station (BS) utilizes only a fraction of the full channel response. The mobile station (MS) of the proposed system selects a single antenna at the receive side based on which the BS adjusts beamforming weights without any overhead to the structure of the BS. Without requiring the singular value decomposition operation, the proposed scheme achieves a performance very close to that of the optimum beamforming system. An exact analysis of the received signal-to-noise ratio for the proposed scheme with 2 receive antennas is also presented. Simulation results show that with much reduced complexity the performance of the proposed beamforming technique is only a few tenth of a dB away from the optimal beamforming system regardless of antenna configuration.

In this paper, we propose a new spatial multiplexing scheme for transmission over flat-fading multiple-input multiple-output (MIMO) channels, which allows a simple maximum-likelihood decoding at the receiver with small feedback information. We begin with a real-valued representation of the complex-valued system model and show that we can achieve orthogonality between transmitted signals by applying a proper rotation to transmitted symbols. Based on the minimum Euclidean distance between received vectors, we also present a simple antenna selection metric for the proposed spatial multiplexing systems. Simulation results demonstrate that our spatial multiplexing system performs close to the optimum closed loop system with much reduced complexity and feedback overhead.