CSI Distribution for Joint Processing in Cooperative Cellular Networks

In the multiuser multiple-input multiple-output (MU-MIMO) system, to reduce the influence of channel correlation on system performance, the base station (BS) should select the appropriate subset of user equipments (UEs) according to their channel state information (CSI). Due to a lack of channel reciprocity, the downlink CSI needs to be fed back to the BS in frequency division duplexing (FDD) mode. Some scholars have exploited kinds of deep neural networks (DNNs) for sensing and recovering CSI. However, user selection after all the CSI is reconstructed by DNNs will bring a great time delay. In this paper, we propose a deep learning-based CSI feedback scheme called US-CsiNet. Based on adversarial autoencoder (AAE), US-CsiNet can explicitly cover user schedule information while representing CSI. At the UE side, the encoder of US-CsiNet maps the CSI into codewords of which part are feature information for user schedule. Then the BS applies these partial codewords to separate the UEs into different groups and select active UEs. Finally, the decoder of AAE reconstructs the CSI of these active UEs. US-CsiNet can not only simplify the user selection process but also guarantee the accuracy of CSI reconstruction. The simulation results show that the proposed approach outperforms maximum channel gain (MCG) user selection algorithms and achieves the nearly same performance with semiorthogonal user selection (SUS) which needs full CSI of all users at the BS.

Accurate channel state information (CSI) at base station (BS) is the key to achieve full multiplexing and diversity gain in traditional multiple-input multiple-output (MIMO) systems. In frequency-division-duplex (FDD) systems, it is obtained through downlink training and uplink feedback. However, the training and feedback overhead in massive MIMO systems is unacceptable for the large number of antennas equipped at BS. To reduce the CSI feedback overhead and improve CSI quantization accuracy, we propose a limited feedback scheme for spatio-temporal correlated massive MIMO channel. The proposed scheme jointly performs Karhunen-Loève transform (KLT) and polar-cap quantization on CSI. For the compactly deployed antennas at BS, there exists spatial correlation in massive MIMO channel. When the user equipment (UE) moves slowly, adjacent channels feature temporal correlation. The high dimensional and spatial correlated CSI is compressed through KLT. Then the compressed CSI is normalized to be isotropic to perform polar-cap quantization. Simulation results show that the proposed scheme is capable of improving the sum-rate performance with limited feedback bits for massive MIMO systems.

Heterogeneous Networks (HetNets) have gained the attraction of the communication industry recently, due to their promising ability to enhance the performance of future broadband Fifth Generation (5G) networks and are integral parts of 5G systems. They can be viewed in multi-dimensional space where, each slice represents a unique tier that has its own Base Station (BS)s and User Equipment (UE)s. Different tiers cooperate with each other for their mutual benefit. Data can be interactively exchanged among the tiers, and UEs have the flexibility to switch between the tiers. The cells in such a heterogeneous cellular networks have variable sizes, shapes, and coverage regions. However, in HetNets with ultra dense BSs, the distance between them gets very small and, they suffer from very high levels of mutual interference. To improve the performance of HetNets, we have done multiple contributions in this dissertation. First, we have developed analytical derivations for optimizing pilot sequence length which is a very crucial factor in acquiring the Channel State Information (CSI) and the channel estimation process in general. Poisson Point Process (PPP) has been widely used to allocate BSs among various tiers so far. However, BS locations obtained using PPP approach may not be optimum to reduce interference. Therefore, in this dissertation, BSs locations are optimized to reduce the interference and improve the coverage and received signal power. Also, we have derived expressions for static UEs coverage probability and network energy efficiency in HetNets. A proper UE association algorithm for HetNets is a great challenge. The classic max-Signal to Interference and Noise Ratio (SINR) or max-received signal strength (RSS) user association algorithms are inappropriate solutions for HetNets as UEs in this context will tend to connect to the Macro BS, which is the one with the highest signal power. A severe load imbalance and significant inefficiency arises and impacts the performance. The aforementioned algorithms tend to associate UEs to BSs with the best received signal power or signal quality. In HetNets, usually Macro BSs are the ones transmitting the strongest signals; hence most UEs tend to associate with the Macro BS leaving Micro BSs with less load. Also, the conventional max-SINR and max-RSS algorithms do not provide adequate results in multi-tier systems. We suggest two centralized algorithms, LSTD and RTLB, for an even UE association to provide fair load distribution. However RTLB outperforms LSTD in real time scenarios as it easily and quickly adapts to rapid network changes. Furthermore, we consider the mobility of nodes. We derive coverage probability for moving UEs considering both handover and no handover scenarios. Proposed algorithms are fast enough to associate the moving users to different Micro and Macro BSs appropriately in real time. Our algorithms are proved to be feasible and provide a path towards attainable future communication systems.

This paper considers a scenario in which multiple device-to-device (D2D) users can reuse the uplink resources of a cellular network to transmit directly to their corresponding receivers. The aggregated interference from the D2D users is limited by applying a threshold on the allowable interference in the base station. The problem is solved under two types of constraints, namely, the peak interference and average interference constraints. In the former, we assume that full channel state information (CSI) is available at the base station, and we optimize the allowable transmit power for the D2D users so that the number of coexisting D2D communications is maximized. We further define a quality-of-service constraint for the D2D users. In practice, however, it is difficult to have complete CSI at the base station as it imposes heavy signaling overhead. Therefore, in the latter scenario, we assume that no knowledge about the location of D2D users and their CSI are available at the base station. This approach does not impose any signaling overhead. Our results show that even with no CSI knowledge, we are able to improve the system performance in terms of throughput by allowing coexisting D2D communications while satisfying the cellular user's constraints.

In a massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The user equipment (UE) needs to estimate CSI and then feeds it back to the BS in the frequency division duplexing (FDD) mode. Effective compression of CSI will significantly reduce the cost of channel feedback, and many deep learning (DL) based channel compression schemes have been proposed to achieve this goal. In this paper, rather than viewing CSI as images as in most existing works, we propose a new perspective of viewing CSI as an information sequence and analogize CSI feedback to a machine translation task. Further, we propose a novel sequence to sequence (Seq2Seq) model for CSI feedback composed of only recurrent neural networks and a small-scale fully connected layer, avoiding the convolution and pooling structure commonly used in current DL-based works. The advantage of this scheme is that it fully integrates the physical characteristics of the MIMO channel in the spatial domain into the structure of the neural network and avoids the loss of some prominent unsmooth or discontinuous features caused by the inappropriate convolution and pooling operation. Simulation results show that the proposed Seq2Seq model outperforms other DL-based CSI compression techniques under various communication scenarios.

Multiple transmit antennas can be employed in a downlink channel to serve multiple users under the assumption of perfect channel state information (CSI) of all users at the base station. In practice, however, the channel knowledge available at the base station is not perfect. For instance, in case of a time division duplex (TDD) system, the transmit CSI is the estimated uplink channel. On the other hand, in a frequency division duplex (FDD) system, feedback from the users is required to obtain transmit CSI. In this case, we assume the transmit CSI consists of a quantized version of the normalized estimated channel vector and the statistics of the channel norm of each user. In this work we consider the transceiver design of the downlink of a multiuser MISO system based on the minimum mean square error (MMSE) criterion with imperfect CSI as discussed above.

This study examines the performance of the multi-user multiple-input-multiple-output (massive MIMO) systems with 1-bit, 2-bit and 3-bit analogue to digital converters (ADCs) under channel correlation and channel state information (CSI) errors. The OFDM modulated uplink with QPSK, 16QAM and 32QAM schemes are considered for the communication between the base station (BS) and the user equipment (UEs). The BS antenna array is assumed to be uniform linear (1D) or uniform rectangular (2D) arrays. Zero-Forcing (ZF) receiver has been employed by the BS due to its simplicity and superior performance over other linear receivers. Furthermore, we consider the square root raised cosine filter (SRRC) to enhance the performance of the low-resolution ADC systems. The results show that the resolution of ADCs mainly determines the modulation scheme and having a high-level channel correlation or CSI mismatch requires a higher resolution ADCs. Furthermore, it has been shown that the SRRC filter enhances the system performance even with low-resolution ADCs.

This paper addresses channel state information (CSI) estimation in a wireless relay network, which consists of a base station (BS), a relay station (RS), and a mobile station (MS). We propose a method for the BS to obtain the instantaneous CSIs of the three links (BS-RS, BS-MS, and RS-MS links) with minimized overhead. The CSI availability plays an important role for the BS to adapt its transmission strategies to the given channel conditions. Our proposed method exploits the fact that the link condition between fixed BS and fixed RS tends to be stable, and the frequent CSI update is not necessary for this link. In order for the BS to obtain the CSI of the distant link (i.e. RS-MS link), RS amplifies a pilot signal received from MS with a pre-defined amplification factor, and forwards it to BS. This enables the BS to obtain the CSI of the RS-MS link based on the received pilot signal and pre-knowledge on the CSI of BS-RS link. We show by computer simulation that our proposed method can reduce the required overhead for CSI estimation with little degradation on the accuracy of the obtained CSI.

Channel state information (CSI) acquisition is one of the major challenges for massive MIMO systems to enable high efficiency MIMO transmissions. Limited feedback schemes, which quantize CSI into limited number of bits and feed these bits back to the base station (BS), are widely used in real-life mobile networks for BSs to obtain downlink CSI. Practical limited feedback schemes usually have large feedback granularity and introduce severe nonlinear quantization error due to the constraint on feedback overhead. It is hard for legacy signal processing technologies to reconstruct CSI accurately for high efficiency downlink transmissions. Different from the legacy signal processing technologies, deep neural networks trained with big data have strong abilities to fit any nonlinear functions and learn the complex signal model behind the big data, which is a useful tool to solve problems that are nonlinear or hard to be described mathematically. Motivated by these observations, we consider a deep learning based approach for BS receivers to reconstruct CSI based on the limited feedback bits from user equipment (UE). The CSI reconstruction is similar with the image super-resolution which reconstructs a high resolution image from a low resolution one based on the structure information learned by the neural network itself. Performance evaluations demonstrate that the proposed method outperforms the legacy algorithms. The improvement on spectrum efficiency is about 25%. It can also reconstruct the CSI with lower density reference signal (RS) than that in current specification, e.g., the RS density can be 1/32 of current one. Therefore, the reference signal and feedback overhead can be reduced with our method. These results show the potential gain of applying deep learning to physical layer signal processing at the receiver side only, which has little air-interface impact and does not require much effort on enhancing network specifications. Therefore, it is feasible to be deployed in current 5th generation mobile networks with the pace of network evolutions, and opens the gate of intelligent physical layer design for future generations of mobile networks.

Closed-loop MIMO system can provide dramatic gains in channel capacity. However, mobile terminal (MT) must feedback the downlink channel state information (CSI) to Base station (BS), and transmitting CSI is a heavy burden consuming considerable uplink bandwidth. To address this bottleneck problem, a zero-overhead downlink CSI feedback scheme is proposed in this paper. After receiving the downlink pilots, MT directly superposes the uplink pilot on the received analog downlink pilot, forms the hybrid pilot (HP), and then transmits HP to BS via uplink pilot channel. Because downlink CSI can be recovered from HP at BS, and HP is fed back to BS via the uplink pilot channel, no extra uplink channel is occupied and the overhead for CSI feedback is zero. Moreover, since no CSI estimation process is performed at MT, the terminal's complexity can be reduced. Numerical Simulations verify that, the proposed downlink CSI feedback has the higher precision than the traditional feedback schemes, and the MSE of CSI feedback can be reduced significantly.

In this paper, we consider a cognitive multi-hop relay secondary user (SU)\nsystem sharing the spectrum with some primary users (PU). The transmit power as\nwell as the hop selection of the cognitive relays can be dynamically adapted\naccording to the local (and causal) knowledge of the instantaneous channel\nstate information (CSI) in the multi-hop SU system. We shall determine a low\ncomplexity, decentralized algorithm to maximize the average end-to-end\nthroughput of the SU system with dynamic spatial reuse. The problem is\nchallenging due to the decentralized requirement as well as the causality\nconstraint on the knowledge of CSI. Furthermore, the problem belongs to the\nclass of stochastic Network Utility Maximization (NUM) problems which is quite\nchallenging. We exploit the time-scale difference between the PU activity and\nthe CSI fluctuations and decompose the problem into a master problem and\nsubproblems. We derive an asymptotically optimal low complexity solution using\ndivide-and-conquer and illustrate that significant performance gain can be\nobtained through dynamic hop selection and power control. The worst case\ncomplexity and memory requirement of the proposed algorithm is O(M^2) and\nO(M^3) respectively, where $M$ is the number of SUs.\n

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.

In this work, we study the non-time-slotted massive-device multiple access (MaDMA) problem where massive user devices transmit sporadic data to a multi-antenna base station (BS). We develop a structured sparsity learning based multiuser detection (SSL-MUD) scheme. By exploiting the structured sparsity naturally embedded in user signals, our SSL-MUD scheme is able to blindly detect the user packets without any prior knowledge of the user activity state (UAS) and the channel state information (CSI), and hence significantly reduces the transmission overhead. For the blind signal detection at the BS, we put forth the turbo bilinear generalized approximate message passing (Turbo-BiG-AMP) algorithm. Simulation results demonstrate that the Turbo-BiG- AMP algorithm significantly outperforms the existing compressed sensing based approach and achieves a performance comparable to that of the oracle linear minimum mean-square error (Oracle- LMMSE) algorithm (which assumes perfect knowledge of UAS and CSI at the BS).

Network MIMO is a technology being developed towards lifting the interference limits on wireless network through collaborative transmissions from multiple base stations. However its great potential is shadowed by the stringent requirement of high speed/capacity backbones facilitating information exchange, including data, control signals, and channel state information amongst the base stations. In this paper, we propose a user-selective collaboration scheme to mitigate the backbone requirement by reducing the information exchange required. We define a certain threshold for users in each base station and only those users exceeding the threshold will be served by multiple base stations collaboratively. The other users will be served by a single base station as in a conventional non-collaborative transmission. The threshold is determined in such a way that the users with collaborative transmissions enjoy maximized sum rates whereas the others without collaborative transmissions sacrifice only marginal potential collaboration gain in terms of achievable maximized sum rates. As the amount of information to be exchanged will be a square function of the number of users served by the collaborative base stations, the user-selective scheme can reduce the backhaul overhead significantly.

In modern wireless systems, the feedback of DownLink (DL) Channel State Information (CSI) from User Equipment (UE) to Base Stations (BS) may require substantial computational and feedback bandwidth overheads. A promising approach to improve feedback efficiency is to leverage side information which is correlated to DL CSI. Despite potential of doing so, critical aspects remain underexplored in current research, particularly the quantification of the benefits and the inherent limitations of utilizing side information. This paper addresses these gaps by introducing a novel algorithm to compute the rate-distortion function for general compression scenarios incorporating side information. We apply this algorithm to the DL CSI feedback problem having UL CSI as the side information and generate rate-distortion functions. Using the estimated rate-distortion functions, we measure the gain of side information over diverse feedback rates and UE mobility profiles. The results reveal that the benefits of leveraging side information are particularly significant for UEs characterized by high mobility and constrained to operate at low feedback overheads.