Block Diagonalization for Inter-cell Interference Mitigation with Limited CSI Feedback

In this work we investigate the degrees of freedom (DoF) performance of the multiple-input multiple-output (MIMO) interference channel (IC) with limited channel state information (CSI) feedback. We generalise two space-time interference alignment (STIA) algorithms to the K user MIMO IC with a transmit power constraint and show how these schemes exploit both current and outdated CSI to achieve an improved DoF performance. We show that by combining the STIA schemes with traditional methods major DoF gain can be achieved for a variety of limited CSI feedback scenarios in comparison to what would be obtained via the use of the traditional techniques on their own. In particular we prove that optimal DoF can still be achieved in situations where current CSI is unavailable for a derived fraction of the time. Finally we validate the promised DoF results via system simulation and show how these DoF gains reflect in terms of sum rate achievable.

This paper investigates a precoding method in downlink multiuser multiple-input multiple-output (MIMO) transmission with multiple base station (BS) cooperation, where each user device basically feeds back the instantaneous channel state information (CSI) to only the nearest BS, but the users near the cell edge additionally feedback the instantaneous CSI to the second nearest BS among the cooperating BSs. Our precoding method is categorized as a form of multi-cell processing (MCP) [5], in which the transmission information to a user is shared by the cooperating BSs in order to utilize fully the degrees of freedom of the spatial channel, and is based on block diagonalization of the channel matrix. However, since some elements of the channel matrix are unknown, we allow partially non-orthogonal transmission. More specifically, we allow inter-user interference to users with limited instantaneous CSI feedback from the channel where the instantaneous CSIs of those users are not obtained at the BSs. The other sources of inter-user interference are set to zero based on the block diagonalization of the channel matrix. The proposed method more efficiently utilizes the degrees of freedom of the spatial channel compared to the case with full orthogonal transmission at the cost of increased inter-user interference. Simulation results show the effectiveness of the proposed method compared to the conventional approaches, which can accommodate the partial CSI feedback scenario, from the viewpoints of the required transmission power and achievable throughput.

In this paper, a comprehensive comparison analysis in terms of outage probability and average symbol error ratio (SER) is presented for cooperative cognitive multiple-input and multiple-output (CC-MIMO) multiuser systems with amplify-and-forward (AF) protocol. Specially, we consider two scenarios where the CC-MIMO multiuser systems have the perfect and imperfect channel state information (CSI). The CC-MIMO multiuser systems consist of one multi-antenna source, one single-antenna relay, and multiple multi-antenna destinations. At the secondary source and destinations, the maximal ratio transmission (MRT) and maximal ratio combining (MRC) are employed, respectively. For such CC-MIMO multiuser systems, we first obtain the exact closed-form expressions of outage probability under the two cases where the CC-MIMO multiuser systems have the perfect and imperfect CSI. Then, to reduce the implementation complexity, the tight lower bounds of outage probability and average SER are derived. Finally, to obtain insight, by using the high signal-to-noise ratio (SNR) approximation, the asymptotic estimations of outage probability are achieved. The numerical results show that the derivations are agreed with the simulations, which validate our derivations. At the same time, the results show that, for the systems without perfect CSI, the achievable diversity order reduces to one, regardless of the number of antennas at the cognitive source and destinations as well as the number of the cognitive destinations. Nevertheless, these key parameters affect the coding gain of the CC-MIMO multiuser systems. When the systems have the perfect CSI (or without feedback delay), the achievable diversity gain is determined by the minimum between the number of source’s antennas and the product of the number of destinations and the number of destination’s antennas. For the effect of PU’s parameters, our results indicate that primary systems only affect the coding gain but not the diversity gain.

Recently, substantial attention has been paid to increase the achievable rates of wireless networks using different kinds of limited channel state information (CSI) feedback. This paper aims to investigate the effect of limited noisy CSI feedback on the achievable rates of power-limited block fading channels constrained to have limited outage probability. Considering different power allocation strategies and various fading conditions, the results are obtained for different outage probabilities and are compared with the case where there is no outage limitation. Moreover, considering different outage probability constraints, the effect of feedback channel noise on the average rates of the forward channel is studied and compared with the ones obtained under noise-free feedback link assumption. Simulation results show that 1) although the outage probability constraint reduces the channel average rate, its influence diminishes as the channel state information feedback or the input power increases, 2) the effect of the outage probability constraint decreases as the forward channel fading severity or the feedback channel noise is reduced.

Recently, substantial attention has been paid to increase the achievable rates of wireless networks using different kinds of limited channel state information (CSI) feedback. This paper aims to investigate the effect of limited CSI feedback on the achievable rates of power-limited block fading channels constrained to have limited outage probability. Considering different power allocation strategies and various fading conditions, the results are obtained for different outage probabilities and are compared with the case where there is no outage limitation. Simulation results show that 1) although the outage probability constraint reduces the channel average rate, its influence diminishes as the channel state information feedback or the input power increases, 2) the effect of the outage probability constraint decreases as the channel fading severity is reduced.

Multicell cooperative processing (MCP) can overcome the detrimental effects of inter-cell interference (ICI). However, since mobile terminals (MTs) need to estimate and feed back channel state information (CSI) related to several cooperative base stations (BSs), reductions in CSI feedback overhead and delay are more demanding in MCP enabled networks. In this paper, a novel overlaid-pilot based CSI feedback scheme for multicell cooperative networks is proposed. MTs directly overlay the received downlink pilot data of all the cooperative BSs on the uplink pilot data and then broadcast it to the cooperative BSs. Each cooperative BS can recover multiple CSIs from the overlaid pilot by a presented overlaid-pilot based channel estimation algorithm. Because the overlaid pilot is fed back on the uplink pilot channel, no extra uplink channel is occupied and the downlink CSI feedback process is with zero-overhead regardless of the number of cooperative BSs. In addition, as it just needs one broadcast to feed back multiple CSIs to BSs, the feedback delay is decreased significantly. Numerical simulations show that the performance of the proposed CSI feedback scheme outperforms the traditional feedback scheme.

Multiple input multiple output (MIMO) processing techniques are of interest for the next generation wireless communication systems due to their potential ability to dramatically improve the system capacity. There have been existed several multi-user MIMO (MU-MIMO) transmit schemes such as zero-forcing dirty paper coding (ZF-DPC), and zero-forcing beamforming (ZFBF). In this paper, we analysis and compare the dynamic transmission schemes based on TDMA and SDMA under perfect and imperfect channel state information (CSI). The system throughput (sum data rate), the optimal user number that can be supported are evaluated under perfect CSI by computer simulations. The effect of imperfect CSI on the throughput of TDMA and SDMA is also investigated. The results show that under perfect channel state information at both transmitter and receiver, the SDMA schemes can achieve a larger throughput than the TDMA scheme because SDMA can fully explore the multiuser diversity by transmitting to the optimal set of users. In contrast, the results show that the system performance of SDMA scheme is sensitive to the channel error.

A channel state information (CSI) feedback algorithm based on scalar quantization is proposed for a multiple-input multiple-output (MIMO) transmission. The performance of the proposed algorithm is evaluated using the effective transmission rate, which incorporates the effect of the CSI feedback overhead on transmission time. Besides that, the power consumed due to the computational complexity of the CSI feedback algorithm is taken into account together with the transmit power when evaluating the energy efficiency. We compare our proposed algorithm to other typical CSI feedback algorithms under correlated channel conditions. Simulation results show that the proposed algorithm enables practical MIMO transmission to achieve an relative transmission rate at 95% of the ideal rate.

Multiple-input multiple-output (MIMO) provides high throughput for WiFi networks, but it also leads to high overhead due to channel state information (CSI) feedback. Based on experiment measurements, this paper shows that MIMO has different feedback requirements when the receiver is rotating compared with when the receiver is in other mobility scenarios. Experiments of four popular Android games show that device rotation accounts for around 50% of the running time for these games, which implies that rotation-awareness could improve WiFi efficiency significantly for these games. We propose rotation-aware WiFi (RoFi) channel feedback to eliminate unnecessary CSI feedback while maintaining high throughput. We show the failure of existing mobility-aware methods, including CSI similarity, time-of-flight (ToF), and compression noise, in distinguishing the mobility status of rotation and mobile. RoFi calculates power delay profile (PDP) similarity for rotation detection and performs feedback compression and rate selection accordingly. To deal with false rotation detection and status transition between rotation and static, RoFi uses the power of the strongest path, which is calculated from PDP, to further refine CSI feedback when necessary. The RoFi design is compatible with legacy 802.11 protocols and is easy to be deployed on existing WiFi systems. Evaluation results show that RoFi reduces 25%–40% overhead with negligible signal-to-noise ratio decrease in rotation scenarios. RoFi also consumes 29%–69% less energy compared with state-of-the-art feedback compression and rate selection algorithms.

Considering virtual cell clusters formed by several overlapping virtual cells in ultra dense network (UDN), virtual cells which are in the same virtual cell cluster coordinate transmissions to reduce inter-virtual cell interference. However, general beamforming scheme design for channel state information (CSI) demand still remains large, and system overhead for high-dimensional channel training and channel state information feedback is explosively-increased. To solve this problem, this paper focuses on hierarchical beamforming scheme based on virtual cell cluster. Specifically, based on perfect intra-cluster CSI, beamforming direction vector is designed by optimizing the achievable rate of each cluster with Zero-Forming block diagonalization method. Based on the large-scale inter-cluster CSI, the strength of beamforming can be devised by solving the sum rate maximization problem and mitigating the residual co-channel interference among different clusters with an iterative power allocation algorithm via a generalized weighted minimum mean square error (WMMSE) approach. Simulation results demonstrate that the proposed scheme achieves significant performance gain and requires less CSI than traditional schemes.

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.

In the presence of perfect channel state information (CSI), the achievable degrees of freedom (DoF) in wireless interference networks can be linearly scaled up with the number of users. Achievability is based on the idea of interference alignment (IA). However, in the presence of imperfect CSI, the sum rate becomes degraded, and full DoF may no longer be achievable. In this paper, the authors propose novel least squares (LS)- and minimum mean square error (MMSE)-based IA schemes that adaptively design beamformers by relying on the availability of imperfect CSI and knowledge of the channel estimation error variance in advance. Interestingly and unlike the other robust algorithms, the proposed adaptive schemes do not impose extra computational complexity compared to their nonadaptive counterparts. It is shown that the LS-based IA is able to outperform interference leakage minimization algorithms under both perfect and imperfect CSI. Furthermore, the authors compare the performance of the proposed MMSE-based IA with maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. The authors show that while under perfect CSI, the MMSE-based IA achieves the same performance as that of Max-SINR, the former outperforms the latter under CSI mismatch. Meanwhile, it is shown that the proposed MMSE-based IA needs less CSI to be available and has less computational complexity compared to Max-SINR.

We consider a precoding method in downlink multiuser multiple-input multiple-output (MIMO) transmission with multiple base station (BS) cooperation where some of the instantaneous channel state information (CSI) is not available at the BSs. The proposed precoding method is based on block diagonalization of the channel matrix. However, since some elements of the channel matrix are unknown, we allow a partially non-orthogonal transmission. More specifically, we allow inter-user interference to users with limited instantaneous CSI feedback from the channel where the instantaneous CSI of that user is not obtained at the BSs. The other inter-user interferences are set to zero based on the block diagonalization of the channel matrix. The proposed method efficiently uses the degree of freedom of the spatial channel compared to the case with full orthogonal transmission at the cost of increased inter-user interference to the users with limited instantaneous CSI feedback. Simulation results show that the proposed method increases the capacity for a given transmission power or decreases the transmission power for a given capacity requirement compared to the case with full orthogonal transmission.

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

Hybrid beamforming offers a low hardware complexity for millimeter wave massive multiple-input multiple-output systems, which is divided among the analog radio frequency precoding and digital baseband precoding. In order to provide channel state information for the base station, channel feedback is essential, especially in frequency division duplexing systems. However, in large antenna systems, the size of channel matrix is huge, and the overhead of feedback complete channel is not acceptable. To reduce the feedback overhead for hybrid beamforming, leveraging the property that the millimeter wave path angles of departure vary more slowly than the path gains, we propose a two-timescale hybrid beamforming/combining algorithm for downlink multi-streams millimeter wave systems. We analyze the performance of the proposed algorithm with perfect channel state information, and the analytical and simulation results show that proposed algorithm can achieve the same performance of full digital singular value decomposition algorithm. We also analyze the rate gap between the proposed algorithm with perfect channel state information and limited feedback. The rate gap caused by quantization of radio frequency and baseband precoder codebooks are decoupled, and the quantization error is analyzed. At last, we provide a closed-form expression of relationship between feedback bits and rate gap.